diff --git a/docs/docs/additional_resources/arxiv_references.mdx b/docs/docs/additional_resources/arxiv_references.mdx index 8b1b84c324a..b9848168e5d 100644 --- a/docs/docs/additional_resources/arxiv_references.mdx +++ b/docs/docs/additional_resources/arxiv_references.mdx @@ -4,8 +4,11 @@ LangChain implements the latest research in the field of Natural Language Proces This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, Templates, and Cookbooks. -From the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. -Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header). +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) ## Summary @@ -23,32 +26,30 @@ Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype | `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 | `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_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://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) -| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler, et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas) -| `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_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...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_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) +| `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://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...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.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) | `2212.07425v3` [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...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), `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/chat/huggingface](https://python.langchain.com/docs/integrations/chat/huggingface), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `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) +| `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/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [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), [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) | `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://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 | `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) +| `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://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/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://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [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...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) | `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) -| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers) +| `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://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [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...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) ## Self-Discover: Large Language Models Self-Compose Reasoning Structures -- **arXiv id:** 2402.03620v1 +- **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. -- **Published Date:** 2024-02-06 -- **URL:** http://arxiv.org/abs/2402.03620v1 - **LangChain:** - **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb) @@ -70,11 +71,9 @@ commonalities with human reasoning patterns. ## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval -- **arXiv id:** 2401.18059v1 +- **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. -- **Published Date:** 2024-01-31 -- **URL:** http://arxiv.org/abs/2401.18059v1 - **LangChain:** - **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb) @@ -96,11 +95,9 @@ benchmark by 20% in absolute accuracy. ## Corrective Retrieval Augmented Generation -- **arXiv id:** 2401.15884v2 +- **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. -- **Published Date:** 2024-01-29 -- **URL:** http://arxiv.org/abs/2401.15884v2 - **LangChain:** - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb) @@ -126,11 +123,9 @@ performance of RAG-based approaches. ## Mixtral of Experts -- **arXiv id:** 2401.04088v1 +- **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. -- **Published Date:** 2024-01-08 -- **URL:** http://arxiv.org/abs/2401.04088v1 - **LangChain:** - **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb) @@ -152,11 +147,9 @@ 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 +- **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. -- **Published Date:** 2023-12-11 -- **URL:** http://arxiv.org/abs/2312.06648v2 - **LangChain:** - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval) @@ -181,11 +174,9 @@ information. ## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models -- **arXiv id:** 2311.09210v1 +- **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. -- **Published Date:** 2023-11-15 -- **URL:** http://arxiv.org/abs/2311.09210v1 - **LangChain:** - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki) @@ -215,11 +206,9 @@ outside the pre-training knowledge scope. ## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection -- **arXiv id:** 2310.11511v1 +- **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. -- **Published Date:** 2023-10-17 -- **URL:** http://arxiv.org/abs/2310.11511v1 - **LangChain:** - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb) @@ -248,11 +237,9 @@ to these models. ## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models -- **arXiv id:** 2310.06117v2 +- **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. -- **Published Date:** 2023-10-09 -- **URL:** http://arxiv.org/abs/2310.06117v2 - **LangChain:** - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting) @@ -271,11 +258,9 @@ and 11% respectively, TimeQA by 27%, and MuSiQue by 7%. ## Llama 2: Open Foundation and Fine-Tuned Chat Models -- **arXiv id:** 2307.09288v2 +- **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. -- **Published Date:** 2023-07-18 -- **URL:** http://arxiv.org/abs/2307.09288v2 - **LangChain:** - **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb) @@ -292,11 +277,9 @@ contribute to the responsible development of LLMs. ## Query Rewriting for Retrieval-Augmented Large Language Models -- **arXiv id:** 2305.14283v3 +- **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. -- **Published Date:** 2023-05-23 -- **URL:** http://arxiv.org/abs/2305.14283v3 - **LangChain:** - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read) @@ -322,11 +305,9 @@ for retrieval-augmented LLM. ## Large Language Model Guided Tree-of-Thought -- **arXiv id:** 2305.08291v1 +- **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 -- **Published Date:** 2023-05-15 -- **URL:** http://arxiv.org/abs/2305.08291v1 - **LangChain:** - **API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot) @@ -352,11 +333,9 @@ 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 +- **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. -- **Published Date:** 2023-05-06 -- **URL:** http://arxiv.org/abs/2305.04091v3 - **LangChain:** - **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb) @@ -383,13 +362,35 @@ Prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem. The code can be found at 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. +- **LangChain:** + + - **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 +amounts of relevance judgments as training data. In this work, we propose +Listwise Reranker with a Large Language Model (LRL), which achieves strong +reranking effectiveness without using any task-specific training data. +Different from the existing pointwise ranking methods, where documents are +scored independently and ranked according to the scores, LRL directly generates +a reordered list of document identifiers given the candidate documents. +Experiments on three TREC web search datasets demonstrate that LRL not only +outperforms zero-shot pointwise methods when reranking first-stage retrieval +results, but can also act as a final-stage reranker to improve the top-ranked +results of a pointwise method for improved efficiency. Additionally, we apply +our approach to subsets of MIRACL, a recent multilingual retrieval dataset, +with results showing its potential to generalize across different languages. + ## Visual Instruction Tuning -- **arXiv id:** 2304.08485v2 +- **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. -- **Published Date:** 2023-04-17 -- **URL:** http://arxiv.org/abs/2304.08485v2 - **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) @@ -412,11 +413,9 @@ publicly available. ## Generative Agents: Interactive Simulacra of Human Behavior -- **arXiv id:** 2304.03442v2 +- **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. -- **Published Date:** 2023-04-07 -- **URL:** http://arxiv.org/abs/2304.03442v2 - **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) @@ -448,11 +447,9 @@ interaction patterns for enabling believable simulations of human behavior. ## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society -- **arXiv id:** 2303.17760v2 +- **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. -- **Published Date:** 2023-03-31 -- **URL:** http://arxiv.org/abs/2303.17760v2 - **LangChain:** - **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb) @@ -478,11 +475,9 @@ 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 +- **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. -- **Published Date:** 2023-03-30 -- **URL:** http://arxiv.org/abs/2303.17580v4 - **LangChain:** - **API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents) @@ -508,40 +503,14 @@ modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence. -## GPT-4 Technical Report - -- **arXiv id:** 2303.08774v6 -- **Title:** GPT-4 Technical Report -- **Authors:** OpenAI, Josh Achiam, Steven Adler, et al. -- **Published Date:** 2023-03-15 -- **URL:** http://arxiv.org/abs/2303.08774v6 -- **LangChain:** - - - **Documentation:** [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas) - -**Abstract:** We report the development of GPT-4, a large-scale, multimodal model which can -accept image and text inputs and produce text outputs. While less capable than -humans in many real-world scenarios, GPT-4 exhibits human-level performance on -various professional and academic benchmarks, including passing a simulated bar -exam with a score around the top 10% of test takers. GPT-4 is a -Transformer-based model pre-trained to predict the next token in a document. -The post-training alignment process results in improved performance on measures -of factuality and adherence to desired behavior. A core component of this -project was developing infrastructure and optimization methods that behave -predictably across a wide range of scales. This allowed us to accurately -predict some aspects of GPT-4's performance based on models trained with no -more than 1/1,000th the compute of GPT-4. - ## A Watermark for Large Language Models -- **arXiv id:** 2301.10226v4 +- **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. -- **Published Date:** 2023-01-24 -- **URL:** http://arxiv.org/abs/2301.10226v4 - **LangChain:** - - **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...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_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) + - **API Reference:** [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...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.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 @@ -559,11 +528,9 @@ family, and discuss robustness and security. ## Precise Zero-Shot Dense Retrieval without Relevance Labels -- **arXiv id:** 2212.10496v1 +- **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. -- **Published Date:** 2022-12-20 -- **URL:** http://arxiv.org/abs/2212.10496v1 - **LangChain:** - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder) @@ -590,11 +557,9 @@ search, QA, fact verification) and languages~(e.g. sw, ko, ja). ## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments -- **arXiv id:** 2212.07425v3 +- **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. -- **Published Date:** 2022-12-12 -- **URL:** http://arxiv.org/abs/2212.07425v3 - **LangChain:** - **API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal) @@ -623,11 +588,9 @@ further work on logical fallacy identification. ## Complementary Explanations for Effective In-Context Learning -- **arXiv id:** 2211.13892v2 +- **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. -- **Published Date:** 2022-11-25 -- **URL:** http://arxiv.org/abs/2211.13892v2 - **LangChain:** - **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) @@ -651,14 +614,12 @@ performance across three real-world tasks on multiple LLMs. ## PAL: Program-aided Language Models -- **arXiv id:** 2211.10435v2 +- **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. -- **Published Date:** 2022-11-18 -- **URL:** http://arxiv.org/abs/2211.10435v2 - **LangChain:** - - **API Reference:** [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), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain) + - **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 @@ -686,15 +647,13 @@ publicly available at http://reasonwithpal.com/ . ## ReAct: Synergizing Reasoning and Acting in Language Models -- **arXiv id:** 2210.03629v3 +- **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. -- **Published Date:** 2022-10-06 -- **URL:** http://arxiv.org/abs/2210.03629v3 - **LangChain:** - - **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/chat/huggingface](https://python.langchain.com/docs/integrations/chat/huggingface), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping) - - **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) + - **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://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [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) **Abstract:** While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their @@ -721,11 +680,9 @@ Project site with code: https://react-lm.github.io ## Deep Lake: a Lakehouse for Deep Learning -- **arXiv id:** 2209.10785v2 +- **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. -- **Published Date:** 2022-09-22 -- **URL:** http://arxiv.org/abs/2209.10785v2 - **LangChain:** - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake) @@ -747,13 +704,43 @@ visualization engine, or (c) deep learning frameworks without sacrificing GPU utilization. Datasets stored in Deep Lake can be accessed from PyTorch, 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. +- **LangChain:** + + - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/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 +often the case that computational and statistical constraints for each +downstream task are unknown. In this context rigid, fixed capacity +representations can be either over or under-accommodating to the task at hand. +This leads us to ask: can we design a flexible representation that can adapt to +multiple downstream tasks with varying computational resources? Our main +contribution is Matryoshka Representation Learning (MRL) which encodes +information at different granularities and allows a single embedding to adapt +to the computational constraints of downstream tasks. MRL minimally modifies +existing representation learning pipelines and imposes no additional cost +during inference and deployment. MRL learns coarse-to-fine representations that +are at least as accurate and rich as independently trained low-dimensional +representations. The flexibility within the learned Matryoshka Representations +offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at +the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale +retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for +long-tail few-shot classification, all while being as robust as the original +representations. Finally, we show that MRL extends seamlessly to web-scale +datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet), +vision + language (ALIGN) and language (BERT). MRL code and pretrained models +are open-sourced at https://github.com/RAIVNLab/MRL. + ## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages -- **arXiv id:** 2205.12654v1 +- **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 -- **Published Date:** 2022-05-25 -- **URL:** http://arxiv.org/abs/2205.12654v1 - **LangChain:** - **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings) @@ -778,14 +765,12 @@ 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 +- **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 -- **Published Date:** 2022-03-15 -- **URL:** http://arxiv.org/abs/2204.00498v1 - **LangChain:** - - **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) + - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL) **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 @@ -797,14 +782,12 @@ few-shot examples. ## Locally Typical Sampling -- **arXiv id:** 2202.00666v5 +- **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. -- **Published Date:** 2022-02-01 -- **URL:** http://arxiv.org/abs/2202.00666v5 - **LangChain:** - - **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) + - **API Reference:** [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...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.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 @@ -829,11 +812,9 @@ reducing degenerate repetitions. ## Learning Transferable Visual Models From Natural Language Supervision -- **arXiv id:** 2103.00020v1 +- **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. -- **Published Date:** 2021-02-26 -- **URL:** http://arxiv.org/abs/2103.00020v1 - **LangChain:** - **API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip) @@ -861,14 +842,12 @@ https://github.com/OpenAI/CLIP. ## CTRL: A Conditional Transformer Language Model for Controllable Generation -- **arXiv id:** 1909.05858v2 +- **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. -- **Published Date:** 2019-09-11 -- **URL:** http://arxiv.org/abs/1909.05858v2 - **LangChain:** - - **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) + - **API Reference:** [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...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.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 @@ -881,32 +860,4 @@ codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl. - -## Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks - -- **arXiv id:** 1908.10084v1 -- **Title:** Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks -- **Authors:** Nils Reimers, Iryna Gurevych -- **Published Date:** 2019-08-27 -- **URL:** http://arxiv.org/abs/1908.10084v1 -- **LangChain:** - - - **Documentation:** [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers) - -**Abstract:** BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new -state-of-the-art performance on sentence-pair regression tasks like semantic -textual similarity (STS). However, it requires that both sentences are fed into -the network, which causes a massive computational overhead: Finding the most -similar pair in a collection of 10,000 sentences requires about 50 million -inference computations (~65 hours) with BERT. The construction of BERT makes it -unsuitable for semantic similarity search as well as for unsupervised tasks -like clustering. - In this publication, we present Sentence-BERT (SBERT), a modification of the -pretrained BERT network that use siamese and triplet network structures to -derive semantically meaningful sentence embeddings that can be compared using -cosine-similarity. This reduces the effort for finding the most similar pair -from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while -maintaining the accuracy from BERT. - We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning -tasks, where it outperforms other state-of-the-art sentence embeddings methods. \ No newline at end of file diff --git a/docs/scripts/arxiv_references.py b/docs/scripts/arxiv_references.py index 97bcc1148ae..d498fcf08d7 100644 --- a/docs/scripts/arxiv_references.py +++ b/docs/scripts/arxiv_references.py @@ -522,8 +522,11 @@ LangChain implements the latest research in the field of Natural Language Proces This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, Templates, and Cookbooks. -From the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. -Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header). +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) ## Summary @@ -604,11 +607,9 @@ Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype f""" ## {paper.title} -- **arXiv id:** {paper.arxiv_id} +- **arXiv id:** [{paper.arxiv_id}]({paper.url}) **Published Date:** {paper.published_date} - **Title:** {paper.title} - **Authors:** {', '.join(paper.authors)} -- **Published Date:** {paper.published_date} -- **URL:** {paper.url} - **LangChain:** {refs}