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docs: added ColBERT reference (#25452)

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Added references to the source papers.
Fixed URL verification code.
Improved arXive page formatting.
Regenerated arXiv page.
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Leonid Ganeline
2024-08-28 19:05:44 -07:00
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@@ -5,51 +5,85 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
Templates, and Cookbooks.
From the opposite direction, scientists use `LangChain` in research and reference it in the research papers.
Here you find papers that reference:
- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)
- [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header)
- [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
`arXiv` papers with references to:
[LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
## Summary
| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
|------------------|---------|-------------------|------------------------|
| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren, et al. | 2024-02-06 | `Cookbook:` [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. | 2024-01-31 | `Cookbook:` [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. | 2024-01-29 | `Cookbook:` [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. | 2024-01-08 | `Cookbook:` [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen, et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al. | 2023-11-15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang, et al. | 2023-10-17 | `Cookbook:` [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al. | 2023-10-09 | `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone, et al. | 2023-07-18 | `Cookbook:` [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He, et al. | 2023-05-23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023-05-15 | `API:` [langchain_experimental.tot](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot), `Cookbook:` [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan, et al. | 2023-05-06 | `Cookbook:` [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al. | 2023-05-03 | `API:` [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
| `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu, et al. | 2023-04-17 | `Cookbook:` [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, et al. | 2023-04-07 | `Cookbook:` [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al. | 2023-03-31 | `Cookbook:` [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan, et al. | 2023-03-30 | `API:` [langchain_experimental.autonomous_agents](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al. | 2023-01-24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin, et al. | 2022-12-20 | `API:` [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://python.langchain.com/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al. | 2022-11-25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou, et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain](https://python.langchain.com/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu, et al. | 2022-10-06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al. | 2022-09-22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege, et al. | 2022-05-26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022-05-25 | `API:` [langchain_community...LaserEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher, et al. | 2022-02-01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy, et al. | 2021-02-26 | `API:` [langchain_experimental.open_clip](https://python.langchain.com/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.open_clip)
| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al. | 2019-09-11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren, et al. | 2024‑02‑06 | `Cookbook:` [Self-Discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. | 2024‑01‑31 | `Cookbook:` [Raptor](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. | 2024‑01‑08 | `Cookbook:` [Together Ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen, et al. | 2023‑12‑11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al. | 2023‑11‑15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang, et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al. | 2023‑10‑09 | `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
| `2307.15337v3` [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](http://arxiv.org/abs/2307.15337v3) | Xuefei Ning, Zinan Lin, Zixuan Zhou, et al. | 2023‑07‑28 | `Template:` [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone, et al. | 2023‑07‑18 | `Cookbook:` [Semi Structured Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt, et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He, et al. | 2023‑05‑23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [Rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023‑05‑15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [Tree Of Thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan, et al. | 2023‑05‑06 | `Cookbook:` [Plan And Execute Agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
| `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu, et al. | 2023‑04‑17 | `Cookbook:` [Semi Structured Multi Modal Rag Llama2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi Structured And Multi Modal Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, et al. | 2023‑04‑07 | `Cookbook:` [Generative Agents Interactive Simulacra Of Human Behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [Multiagent Bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al. | 2023‑03‑31 | `Cookbook:` [Camel Role Playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan, et al. | 2023‑03‑30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [Hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al. | 2023‑01‑24 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin, et al. | 2022‑12‑20 | `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al. | 2022‑12‑12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al. | 2022‑11‑25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou, et al. | 2022‑11‑18 | `API:` [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [Program Aided Language Model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu, et al. | 2022‑10‑06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege, et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa), `API:` [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher, et al. | 2022‑02‑01 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy, et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al. | 2019‑09‑11 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
- **Authors:** Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.
- **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2) **Published Date:** 2024-03-21
- **LangChain:**
- **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
**Abstract:** Retrieval-Augmented Large Language Models (LLMs), which incorporate the
non-parametric knowledge from external knowledge bases into LLMs, have emerged
as a promising approach to enhancing response accuracy in several tasks, such
as Question-Answering (QA). However, even though there are various approaches
dealing with queries of different complexities, they either handle simple
queries with unnecessary computational overhead or fail to adequately address
complex multi-step queries; yet, not all user requests fall into only one of
the simple or complex categories. In this work, we propose a novel adaptive QA
framework, that can dynamically select the most suitable strategy for
(retrieval-augmented) LLMs from the simplest to the most sophisticated ones
based on the query complexity. Also, this selection process is operationalized
with a classifier, which is a smaller LM trained to predict the complexity
level of incoming queries with automatically collected labels, obtained from
actual predicted outcomes of models and inherent inductive biases in datasets.
This approach offers a balanced strategy, seamlessly adapting between the
iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval
methods, in response to a range of query complexities. We validate our model on
a set of open-domain QA datasets, covering multiple query complexities, and
show that ours enhances the overall efficiency and accuracy of QA systems,
compared to relevant baselines including the adaptive retrieval approaches.
Code is available at: https://github.com/starsuzi/Adaptive-RAG.
## Self-Discover: Large Language Models Self-Compose Reasoning Structures
- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1) **Published Date:** 2024-02-06
- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
- **Authors:** Pei Zhou, Jay Pujara, Xiang Ren, et al.
- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1) **Published Date:** 2024-02-06
- **LangChain:**
- **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
@@ -71,9 +105,8 @@ commonalities with human reasoning patterns.
## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1) **Published Date:** 2024-01-31
- **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
- **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli, et al.
- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1) **Published Date:** 2024-01-31
- **LangChain:**
- **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
@@ -95,11 +128,11 @@ benchmark by 20% in absolute accuracy.
## Corrective Retrieval Augmented Generation
- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2) **Published Date:** 2024-01-29
- **Title:** Corrective Retrieval Augmented Generation
- **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.
- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2) **Published Date:** 2024-01-29
- **LangChain:**
- **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
- **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
**Abstract:** Large language models (LLMs) inevitably exhibit hallucinations since the
@@ -121,11 +154,36 @@ RAG-based approaches. Experiments on four datasets covering short- and
long-form generation tasks show that CRAG can significantly improve the
performance of RAG-based approaches.
## Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
- **Authors:** Tal Ridnik, Dedy Kredo, Itamar Friedman
- **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1) **Published Date:** 2024-01-16
- **LangChain:**
- **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
**Abstract:** Code generation problems differ from common natural language problems - they
require matching the exact syntax of the target language, identifying happy
paths and edge cases, paying attention to numerous small details in the problem
spec, and addressing other code-specific issues and requirements. Hence, many
of the optimizations and tricks that have been successful in natural language
generation may not be effective for code tasks. In this work, we propose a new
approach to code generation by LLMs, which we call AlphaCodium - a test-based,
multi-stage, code-oriented iterative flow, that improves the performances of
LLMs on code problems. We tested AlphaCodium on a challenging code generation
dataset called CodeContests, which includes competitive programming problems
from platforms such as Codeforces. The proposed flow consistently and
significantly improves results. On the validation set, for example, GPT-4
accuracy (pass@5) increased from 19% with a single well-designed direct prompt
to 44% with the AlphaCodium flow. Many of the principles and best practices
acquired in this work, we believe, are broadly applicable to general code
generation tasks. Full implementation is available at:
https://github.com/Codium-ai/AlphaCodium
## Mixtral of Experts
- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1) **Published Date:** 2024-01-08
- **Title:** Mixtral of Experts
- **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al.
- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1) **Published Date:** 2024-01-08
- **LangChain:**
- **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
@@ -147,9 +205,8 @@ the base and instruct models are released under the Apache 2.0 license.
## Dense X Retrieval: What Retrieval Granularity Should We Use?
- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2) **Published Date:** 2023-12-11
- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
- **Authors:** Tong Chen, Hongwei Wang, Sihao Chen, et al.
- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2) **Published Date:** 2023-12-11
- **LangChain:**
- **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
@@ -174,9 +231,8 @@ information.
## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1) **Published Date:** 2023-11-15
- **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
- **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al.
- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1) **Published Date:** 2023-11-15
- **LangChain:**
- **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
@@ -206,11 +262,11 @@ outside the pre-training knowledge scope.
## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1) **Published Date:** 2023-10-17
- **Title:** Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
- **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang, et al.
- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1) **Published Date:** 2023-10-17
- **LangChain:**
- **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
- **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
**Abstract:** Despite their remarkable capabilities, large language models (LLMs) often
@@ -237,9 +293,8 @@ to these models.
## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2) **Published Date:** 2023-10-09
- **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
- **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al.
- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2) **Published Date:** 2023-10-09
- **LangChain:**
- **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
@@ -256,11 +311,31 @@ including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back
Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7%
and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
## Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
- **Authors:** Xuefei Ning, Zinan Lin, Zixuan Zhou, et al.
- **arXiv id:** [2307.15337v3](http://arxiv.org/abs/2307.15337v3) **Published Date:** 2023-07-28
- **LangChain:**
- **Template:** [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
**Abstract:** This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the
skeleton of the answer, and then conducts parallel API calls or batched
decoding to complete the contents of each skeleton point in parallel. Not only
does SoT provide considerable speed-ups across 12 LLMs, but it can also
potentially improve the answer quality on several question categories. SoT is
an initial attempt at data-centric optimization for inference efficiency, and
showcases the potential of eliciting high-quality answers by explicitly
planning the answer structure in language.
## Llama 2: Open Foundation and Fine-Tuned Chat Models
- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2) **Published Date:** 2023-07-18
- **Title:** Llama 2: Open Foundation and Fine-Tuned Chat Models
- **Authors:** Hugo Touvron, Louis Martin, Kevin Stone, et al.
- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2) **Published Date:** 2023-07-18
- **LangChain:**
- **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
@@ -275,11 +350,32 @@ detailed description of our approach to fine-tuning and safety improvements of
Llama 2-Chat in order to enable the community to build on our work and
contribute to the responsible development of LLMs.
## Lost in the Middle: How Language Models Use Long Contexts
- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt, et al.
- **arXiv id:** [2307.03172v3](http://arxiv.org/abs/2307.03172v3) **Published Date:** 2023-07-06
- **LangChain:**
- **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
**Abstract:** While recent language models have the ability to take long contexts as input,
relatively little is known about how well they use longer context. We analyze
the performance of language models on two tasks that require identifying
relevant information in their input contexts: multi-document question answering
and key-value retrieval. We find that performance can degrade significantly
when changing the position of relevant information, indicating that current
language models do not robustly make use of information in long input contexts.
In particular, we observe that performance is often highest when relevant
information occurs at the beginning or end of the input context, and
significantly degrades when models must access relevant information in the
middle of long contexts, even for explicitly long-context models. Our analysis
provides a better understanding of how language models use their input context
and provides new evaluation protocols for future long-context language models.
## Query Rewriting for Retrieval-Augmented Large Language Models
- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3) **Published Date:** 2023-05-23
- **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
- **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He, et al.
- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3) **Published Date:** 2023-05-23
- **LangChain:**
- **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
@@ -305,12 +401,11 @@ for retrieval-augmented LLM.
## Large Language Model Guided Tree-of-Thought
- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1) **Published Date:** 2023-05-15
- **Title:** Large Language Model Guided Tree-of-Thought
- **Authors:** Jieyi Long
- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1) **Published Date:** 2023-05-15
- **LangChain:**
- **API Reference:** [langchain_experimental.tot](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot)
- **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
- **Cookbook:** [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
**Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
@@ -333,9 +428,8 @@ implementation of the ToT-based Sudoku solver is available on GitHub:
## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3) **Published Date:** 2023-05-06
- **Title:** Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
- **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan, et al.
- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3) **Published Date:** 2023-05-06
- **LangChain:**
- **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
@@ -364,12 +458,12 @@ https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
## Zero-Shot Listwise Document Reranking with a Large Language Model
- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1) **Published Date:** 2023-05-03
- **Title:** Zero-Shot Listwise Document Reranking with a Large Language Model
- **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al.
- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1) **Published Date:** 2023-05-03
- **LangChain:**
- **API Reference:** [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
- **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression)
- **API Reference:** [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
**Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
have shown success in multi-stage text ranking tasks, but they require large
@@ -388,12 +482,11 @@ with results showing its potential to generalize across different languages.
## Visual Instruction Tuning
- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2) **Published Date:** 2023-04-17
- **Title:** Visual Instruction Tuning
- **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu, et al.
- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2) **Published Date:** 2023-04-17
- **LangChain:**
- **Cookbook:** [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb)
- **Cookbook:** [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
**Abstract:** Instruction tuning large language models (LLMs) using machine-generated
instruction-following data has improved zero-shot capabilities on new tasks,
@@ -413,12 +506,11 @@ publicly available.
## Generative Agents: Interactive Simulacra of Human Behavior
- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2) **Published Date:** 2023-04-07
- **Title:** Generative Agents: Interactive Simulacra of Human Behavior
- **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, et al.
- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2) **Published Date:** 2023-04-07
- **LangChain:**
- **Cookbook:** [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
- **Cookbook:** [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
**Abstract:** Believable proxies of human behavior can empower interactive applications
ranging from immersive environments to rehearsal spaces for interpersonal
@@ -447,9 +539,8 @@ interaction patterns for enabling believable simulations of human behavior.
## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2) **Published Date:** 2023-03-31
- **Title:** CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.
- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2) **Published Date:** 2023-03-31
- **LangChain:**
- **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
@@ -475,12 +566,11 @@ agents and beyond: https://github.com/camel-ai/camel.
## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4) **Published Date:** 2023-03-30
- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- **Authors:** Yongliang Shen, Kaitao Song, Xu Tan, et al.
- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4) **Published Date:** 2023-03-30
- **LangChain:**
- **API Reference:** [langchain_experimental.autonomous_agents](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents)
- **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
- **Cookbook:** [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
**Abstract:** Solving complicated AI tasks with different domains and modalities is a key
@@ -505,12 +595,11 @@ realization of artificial general intelligence.
## A Watermark for Large Language Models
- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4) **Published Date:** 2023-01-24
- **Title:** A Watermark for Large Language Models
- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.
- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4) **Published Date:** 2023-01-24
- **LangChain:**
- **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
- **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
**Abstract:** Potential harms of large language models can be mitigated by watermarking
model output, i.e., embedding signals into generated text that are invisible to
@@ -528,12 +617,11 @@ family, and discuss robustness and security.
## Precise Zero-Shot Dense Retrieval without Relevance Labels
- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1) **Published Date:** 2022-12-20
- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
- **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin, et al.
- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1) **Published Date:** 2022-12-20
- **LangChain:**
- **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
- **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
- **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
- **Cookbook:** [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
@@ -555,14 +643,40 @@ state-of-the-art unsupervised dense retriever Contriever and shows strong
performance comparable to fine-tuned retrievers, across various tasks (e.g. web
search, QA, fact verification) and languages~(e.g. sw, ko, ja).
## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
## Constitutional AI: Harmlessness from AI Feedback
- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3) **Published Date:** 2022-12-12
- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al.
- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.
- **arXiv id:** [2212.08073v1](http://arxiv.org/abs/2212.08073v1) **Published Date:** 2022-12-15
- **LangChain:**
- **API Reference:** [langchain_experimental.fallacy_removal](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.fallacy_removal)
- **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
**Abstract:** As AI systems become more capable, we would like to enlist their help to
supervise other AIs. We experiment with methods for training a harmless AI
assistant through self-improvement, without any human labels identifying
harmful outputs. The only human oversight is provided through a list of rules
or principles, and so we refer to the method as 'Constitutional AI'. The
process involves both a supervised learning and a reinforcement learning phase.
In the supervised phase we sample from an initial model, then generate
self-critiques and revisions, and then finetune the original model on revised
responses. In the RL phase, we sample from the finetuned model, use a model to
evaluate which of the two samples is better, and then train a preference model
from this dataset of AI preferences. We then train with RL using the preference
model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
result we are able to train a harmless but non-evasive AI assistant that
engages with harmful queries by explaining its objections to them. Both the SL
and RL methods can leverage chain-of-thought style reasoning to improve the
human-judged performance and transparency of AI decision making. These methods
make it possible to control AI behavior more precisely and with far fewer human
labels.
## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al.
- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3) **Published Date:** 2022-12-12
- **LangChain:**
- **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
**Abstract:** The spread of misinformation, propaganda, and flawed argumentation has been
amplified in the Internet era. Given the volume of data and the subtlety of
@@ -588,12 +702,11 @@ further work on logical fallacy identification.
## Complementary Explanations for Effective In-Context Learning
- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2) **Published Date:** 2022-11-25
- **Title:** Complementary Explanations for Effective In-Context Learning
- **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.
- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2) **Published Date:** 2022-11-25
- **LangChain:**
- **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
- **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
**Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
learning from explanations in prompts, but there has been limited understanding
@@ -614,12 +727,11 @@ performance across three real-world tasks on multiple LLMs.
## PAL: Program-aided Language Models
- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2) **Published Date:** 2022-11-18
- **Title:** PAL: Program-aided Language Models
- **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou, et al.
- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2) **Published Date:** 2022-11-18
- **LangChain:**
- **API Reference:** [langchain_experimental.pal_chain](https://python.langchain.com/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
- **API Reference:** [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
- **Cookbook:** [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
**Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
@@ -647,13 +759,12 @@ publicly available at http://reasonwithpal.com/ .
## ReAct: Synergizing Reasoning and Acting in Language Models
- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3) **Published Date:** 2022-10-06
- **Title:** ReAct: Synergizing Reasoning and Acting in Language Models
- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.
- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3) **Published Date:** 2022-10-06
- **LangChain:**
- **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping)
- **API Reference:** [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
- **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
- **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
**Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
across tasks in language understanding and interactive decision making, their
@@ -680,12 +791,11 @@ Project site with code: https://react-lm.github.io
## Deep Lake: a Lakehouse for Deep Learning
- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2) **Published Date:** 2022-09-22
- **Title:** Deep Lake: a Lakehouse for Deep Learning
- **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al.
- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2) **Published Date:** 2022-09-22
- **LangChain:**
- **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
- **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
**Abstract:** Traditional data lakes provide critical data infrastructure for analytical
workloads by enabling time travel, running SQL queries, ingesting data with
@@ -706,12 +816,11 @@ TensorFlow, JAX, and integrate with numerous MLOps tools.
## Matryoshka Representation Learning
- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4) **Published Date:** 2022-05-26
- **Title:** Matryoshka Representation Learning
- **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege, et al.
- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4) **Published Date:** 2022-05-26
- **LangChain:**
- **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
- **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
**Abstract:** Learned representations are a central component in modern ML systems, serving
a multitude of downstream tasks. When training such representations, it is
@@ -738,12 +847,11 @@ are open-sourced at https://github.com/RAIVNLab/MRL.
## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1) **Published Date:** 2022-05-25
- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
- **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1) **Published Date:** 2022-05-25
- **LangChain:**
- **API Reference:** [langchain_community...LaserEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
- **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
**Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
languages is challenging, in particular to cover the long tail of low-resource
@@ -765,12 +873,12 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
## Evaluating the Text-to-SQL Capabilities of Large Language Models
- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1) **Published Date:** 2022-03-15
- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
- **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1) **Published Date:** 2022-03-15
- **LangChain:**
- **API Reference:** [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
- **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa)
- **API Reference:** [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
**Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
language model. We find that, without any finetuning, Codex is a strong
@@ -782,12 +890,11 @@ few-shot examples.
## Locally Typical Sampling
- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5) **Published Date:** 2022-02-01
- **Title:** Locally Typical Sampling
- **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher, et al.
- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5) **Published Date:** 2022-02-01
- **LangChain:**
- **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
- **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
**Abstract:** Today's probabilistic language generators fall short when it comes to
producing coherent and fluent text despite the fact that the underlying models
@@ -810,14 +917,35 @@ locally typical sampling offers competitive performance (in both abstractive
summarization and story generation) in terms of quality while consistently
reducing degenerate repetitions.
## Learning Transferable Visual Models From Natural Language Supervision
## ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1) **Published Date:** 2021-02-26
- **Title:** Learning Transferable Visual Models From Natural Language Supervision
- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy, et al.
- **Authors:** Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.
- **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3) **Published Date:** 2021-12-02
- **LangChain:**
- **API Reference:** [langchain_experimental.open_clip](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.open_clip)
- **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
**Abstract:** Neural information retrieval (IR) has greatly advanced search and other
knowledge-intensive language tasks. While many neural IR methods encode queries
and documents into single-vector representations, late interaction models
produce multi-vector representations at the granularity of each token and
decompose relevance modeling into scalable token-level computations. This
decomposition has been shown to make late interaction more effective, but it
inflates the space footprint of these models by an order of magnitude. In this
work, we introduce ColBERTv2, a retriever that couples an aggressive residual
compression mechanism with a denoised supervision strategy to simultaneously
improve the quality and space footprint of late interaction. We evaluate
ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art
quality within and outside the training domain while reducing the space
footprint of late interaction models by 6--10$\times$.
## Learning Transferable Visual Models From Natural Language Supervision
- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy, et al.
- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1) **Published Date:** 2021-02-26
- **LangChain:**
- **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
**Abstract:** State-of-the-art computer vision systems are trained to predict a fixed set
of predetermined object categories. This restricted form of supervision limits
@@ -842,12 +970,11 @@ https://github.com/OpenAI/CLIP.
## CTRL: A Conditional Transformer Language Model for Controllable Generation
- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2) **Published Date:** 2019-09-11
- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.
- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2) **Published Date:** 2019-09-11
- **LangChain:**
- **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
- **API Reference:** [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
**Abstract:** Large-scale language models show promising text generation capabilities, but
users cannot easily control particular aspects of the generated text. We

7
docs/docs/integrations/providers/dspy.ipynb
View File

@@ -7,7 +7,7 @@
"source": [
"# DSPy\n",
"\n",
"[DSPy](https://github.com/stanfordnlp/dspy) is a fantastic framework for LLMs that introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.\n",
">[DSPy](https://github.com/stanfordnlp/dspy) is a fantastic framework for LLMs that introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.\n",
"\n",
"Thanks to [Omar Khattab](https://twitter.com/lateinteraction) we have an integration! It works with any LCEL chains with some minor modifications.\n",
"\n",
@@ -17,6 +17,9 @@
"\n",
"Let's take a look at an example. In this example we will make a simple RAG pipeline. We will use DSPy to \"compile\" our program and learn an optimized prompt.\n",
"\n",
"This example uses the `ColBERTv2` model.\n",
"See the [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) paper.\n",
"\n",
"\n",
"## Install dependencies\n",
"\n",
@@ -1175,7 +1178,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
"version": "3.10.12"
}
},
"nbformat": 4,

6
docs/docs/integrations/providers/ragatouille.ipynb
View File

@@ -7,7 +7,9 @@
"source": [
"# RAGatouille\n",
"\n",
"[RAGatouille](https://github.com/bclavie/RAGatouille) makes it as simple as can be to use ColBERT! [ColBERT](https://github.com/stanford-futuredata/ColBERT) is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.\n",
">[RAGatouille](https://github.com/bclavie/RAGatouille) makes it as simple as can be to use `ColBERT`! [ColBERT](https://github.com/stanford-futuredata/ColBERT) is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.\n",
">\n",
">See the [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) paper.\n",
"\n",
"There are multiple ways that we can use RAGatouille.\n",
"\n",
@@ -258,7 +260,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
"version": "3.10.12"
}
},
"nbformat": 4,

2
docs/docs/integrations/retrievers/ragatouille.ipynb
View File

@@ -11,6 +11,8 @@
">[RAGatouille](https://github.com/bclavie/RAGatouille) makes it as simple as can be to use `ColBERT`!\n",
">\n",
">[ColBERT](https://github.com/stanford-futuredata/ColBERT) is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.\n",
">\n",
">See the [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) paper.\n",
"\n",
"We can use this as a [retriever](/docs/how_to#retrievers). It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/docs/how_to#qa-with-rag) to learn how to use this vector store as part of a larger chain.\n",
"\n",

16
docs/scripts/arxiv_references.py
View File

@@ -397,7 +397,7 @@ class ArxivAPIWrapper(BaseModel):
def _format_doc_url(doc_path: str) -> str:
return f"https://{LANGCHAIN_PYTHON_URL}/{doc_path}"
return f"https://{LANGCHAIN_PYTHON_URL}/v0.2/{doc_path}"
def _format_api_ref_url(doc_path: str, compact: bool = False) -> str:
@@ -523,10 +523,9 @@ This page contains `arXiv` papers referenced in the LangChain Documentation, API
Templates, and Cookbooks.
From the opposite direction, scientists use `LangChain` in research and reference it in the research papers.
Here you find papers that reference:
- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)
- [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header)
- [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
`arXiv` papers with references to:
[LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
## Summary
@@ -564,7 +563,7 @@ Here you find papers that reference:
refs += [
"`Cookbook:` "
+ ", ".join(
f"[{key}]({url})"
f"[{str(key).replace('_', ' ').title()}]({url})"
for key, url in paper.referencing_cookbook2url.items()
)
]
@@ -572,7 +571,7 @@ Here you find papers that reference:
title_link = f"[{paper.title}]({paper.url})"
f.write(
f"| {' | '.join([f'`{paper.arxiv_id}` {title_link}', ', '.join(paper.authors), paper.published_date, refs_str])}\n"
f"| {' | '.join([f'`{paper.arxiv_id}` {title_link}', ', '.join(paper.authors), paper.published_date.replace('-', '‑'), refs_str])}\n"
)
for paper in papers:
@@ -607,9 +606,8 @@ Here you find papers that reference:
f"""
## {paper.title}
- **arXiv id:** [{paper.arxiv_id}]({paper.url}) **Published Date:** {paper.published_date}
- **Title:** {paper.title}
- **Authors:** {', '.join(paper.authors)}
- **arXiv id:** [{paper.arxiv_id}]({paper.url}) **Published Date:** {paper.published_date}
- **LangChain:**
{refs}