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docs: arxiv page update (#25450)
Added `arxive` papers that use `LangGraph` or `LangSmith`. Improved the page formatting.
This commit is contained in:
Leonid Ganeline
GitHub
parent
5150ec3a04
commit
8eb63a609e
@ -4,8 +4,11 @@ LangChain implements the latest research in the field of Natural Language Proces
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This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
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Templates, and Cookbooks.
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From the opposite direction, scientists use LangChain in research and reference LangChain in the research papers.
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Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header).
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From the opposite direction, scientists use `LangChain` in research and reference it in the research papers.
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Here you find papers that reference:
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- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)
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- [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header)
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- [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
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## Summary
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@ -23,32 +26,30 @@ Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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| `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)
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## Self-Discover: Large Language Models Self-Compose Reasoning Structures
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- **arXiv id:** 2402.03620v1
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- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1) **Published Date:** 2024-02-06
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- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
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- **Authors:** Pei Zhou, Jay Pujara, Xiang Ren, et al.
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- **Published Date:** 2024-02-06
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- **URL:** http://arxiv.org/abs/2402.03620v1
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- **LangChain:**
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- **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
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## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
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- **arXiv id:** 2401.18059v1
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- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1) **Published Date:** 2024-01-31
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- **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
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- **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli, et al.
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- **Published Date:** 2024-01-31
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- **URL:** http://arxiv.org/abs/2401.18059v1
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- **LangChain:**
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- **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
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@ -96,11 +95,9 @@ benchmark by 20% in absolute accuracy.
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## Corrective Retrieval Augmented Generation
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- **arXiv id:** 2401.15884v2
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- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2) **Published Date:** 2024-01-29
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- **Title:** Corrective Retrieval Augmented Generation
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- **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.
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- **Published Date:** 2024-01-29
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- **URL:** http://arxiv.org/abs/2401.15884v2
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- **LangChain:**
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- **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
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@ -126,11 +123,9 @@ performance of RAG-based approaches.
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## Mixtral of Experts
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- **arXiv id:** 2401.04088v1
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- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1) **Published Date:** 2024-01-08
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- **Title:** Mixtral of Experts
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- **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al.
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- **Published Date:** 2024-01-08
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- **URL:** http://arxiv.org/abs/2401.04088v1
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- **LangChain:**
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- **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
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@ -152,11 +147,9 @@ the base and instruct models are released under the Apache 2.0 license.
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## Dense X Retrieval: What Retrieval Granularity Should We Use?
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- **arXiv id:** 2312.06648v2
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- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2) **Published Date:** 2023-12-11
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- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
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- **Authors:** Tong Chen, Hongwei Wang, Sihao Chen, et al.
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- **Published Date:** 2023-12-11
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- **URL:** http://arxiv.org/abs/2312.06648v2
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- **LangChain:**
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- **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
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@ -181,11 +174,9 @@ information.
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## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
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|
||||
- **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
|
||||
@ -882,31 +861,3 @@ 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.
|
||||
|
||||
@ -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}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user