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+# arXiv
+            
+LangChain implements the latest research in the field of Natural Language Processing.
+This page contains `arXiv` papers referenced in the LangChain Documentation and API Reference.
+
+## Summary
+
+| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation and API Reference |
+|------------------|---------|-------------------|-------------------------|
+| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023-07-06 | `Docs:` [docs/modules/data_connection/retrievers/long_context_reorder](https://python.langchain.com/docs/modules/data_connection/retrievers/long_context_reorder)
+| `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)
+| `2305.06983v2` [Active Retrieval Augmented Generation](http://arxiv.org/abs/2305.06983v2) | Zhengbao Jiang, Frank F. Xu, Luyu Gao,  et al. | 2023-05-11 | `Docs:` [docs/modules/chains](https://python.langchain.com/docs/modules/chains)
+| `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)
+| `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.llms...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), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community.llms...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)
+| `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 | `Docs:` [docs/use_cases/query_analysis/techniques/hyde](https://python.langchain.com/docs/use_cases/query_analysis/techniques/hyde), `API:` [langchain.chains...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
+| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022-12-15 | `Docs:` [docs/guides/productionization/evaluation/string/criteria_eval_chain](https://python.langchain.com/docs/guides/productionization/evaluation/string/criteria_eval_chain)
+| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
+| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022-11-25 | `API:` [langchain_core.example_selectors...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
+| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain...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)
+| `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)
+| `2205.12654v1` [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.embeddings...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
+| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `Docs:` [docs/use_cases/sql/quickstart](https://python.langchain.com/docs/use_cases/sql/quickstart), `API:` [langchain_community.utilities...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities...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_community.llms...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), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `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.llms...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), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+| `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)
+
+## Lost in the Middle: How Language Models Use Long Contexts
+
+- **arXiv id:** 2307.03172v3
+- **Title:** Lost in the Middle: How Language Models Use Long Contexts
+- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt,  et al.
+- **Published Date:** 2023-07-06
+- **URL:** http://arxiv.org/abs/2307.03172v3
+- **LangChain Documentation:** [docs/modules/data_connection/retrievers/long_context_reorder](https://python.langchain.com/docs/modules/data_connection/retrievers/long_context_reorder)
+
+
+**Abstract:** While recent language models have the ability to take long contexts as input,
+relatively little is known about how well they use longer context. We analyze
+the performance of language models on two tasks that require identifying
+relevant information in their input contexts: multi-document question answering
+and key-value retrieval. We find that performance can degrade significantly
+when changing the position of relevant information, indicating that current
+language models do not robustly make use of information in long input contexts.
+In particular, we observe that performance is often highest when relevant
+information occurs at the beginning or end of the input context, and
+significantly degrades when models must access relevant information in the
+middle of long contexts, even for explicitly long-context models. Our analysis
+provides a better understanding of how language models use their input context
+and provides new evaluation protocols for future long-context language models.
+                
+## Large Language Model Guided Tree-of-Thought
+
+- **arXiv id:** 2305.08291v1
+- **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)
+
+**Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
+approach aimed at improving the problem-solving capabilities of auto-regressive
+large language models (LLMs). The ToT technique is inspired by the human mind's
+approach for solving complex reasoning tasks through trial and error. In this
+process, the human mind explores the solution space through a tree-like thought
+process, allowing for backtracking when necessary. To implement ToT as a
+software system, we augment an LLM with additional modules including a prompter
+agent, a checker module, a memory module, and a ToT controller. In order to
+solve a given problem, these modules engage in a multi-round conversation with
+the LLM. The memory module records the conversation and state history of the
+problem solving process, which allows the system to backtrack to the previous
+steps of the thought-process and explore other directions from there. To verify
+the effectiveness of the proposed technique, we implemented a ToT-based solver
+for the Sudoku Puzzle. Experimental results show that the ToT framework can
+significantly increase the success rate of Sudoku puzzle solving. Our
+implementation of the ToT-based Sudoku solver is available on GitHub:
+\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
+                
+## Active Retrieval Augmented Generation
+
+- **arXiv id:** 2305.06983v2
+- **Title:** Active Retrieval Augmented Generation
+- **Authors:** Zhengbao Jiang, Frank F. Xu, Luyu Gao,  et al.
+- **Published Date:** 2023-05-11
+- **URL:** http://arxiv.org/abs/2305.06983v2
+- **LangChain Documentation:** [docs/modules/chains](https://python.langchain.com/docs/modules/chains)
+
+
+**Abstract:** Despite the remarkable ability of large language models (LMs) to comprehend
+and generate language, they have a tendency to hallucinate and create factually
+inaccurate output. Augmenting LMs by retrieving information from external
+knowledge resources is one promising solution. Most existing retrieval
+augmented LMs employ a retrieve-and-generate setup that only retrieves
+information once based on the input. This is limiting, however, in more general
+scenarios involving generation of long texts, where continually gathering
+information throughout generation is essential. In this work, we provide a
+generalized view of active retrieval augmented generation, methods that
+actively decide when and what to retrieve across the course of the generation.
+We propose Forward-Looking Active REtrieval augmented generation (FLARE), a
+generic method which iteratively uses a prediction of the upcoming sentence to
+anticipate future content, which is then utilized as a query to retrieve
+relevant documents to regenerate the sentence if it contains low-confidence
+tokens. We test FLARE along with baselines comprehensively over 4 long-form
+knowledge-intensive generation tasks/datasets. FLARE achieves superior or
+competitive performance on all tasks, demonstrating the effectiveness of our
+method. Code and datasets are available at https://github.com/jzbjyb/FLARE.
+                
+## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
+
+- **arXiv id:** 2303.17580v4
+- **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)
+
+**Abstract:** Solving complicated AI tasks with different domains and modalities is a key
+step toward artificial general intelligence. While there are numerous AI models
+available for various domains and modalities, they cannot handle complicated AI
+tasks autonomously. Considering large language models (LLMs) have exhibited
+exceptional abilities in language understanding, generation, interaction, and
+reasoning, we advocate that LLMs could act as a controller to manage existing
+AI models to solve complicated AI tasks, with language serving as a generic
+interface to empower this. Based on this philosophy, we present HuggingGPT, an
+LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI
+models in machine learning communities (e.g., Hugging Face) to solve AI tasks.
+Specifically, we use ChatGPT to conduct task planning when receiving a user
+request, select models according to their function descriptions available in
+Hugging Face, execute each subtask with the selected AI model, and summarize
+the response according to the execution results. By leveraging the strong
+language capability of ChatGPT and abundant AI models in Hugging Face,
+HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different
+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
+- **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.llms.huggingface_text_gen_inference.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), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community.llms.oci_data_science_model_deployment_endpoint.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)
+
+**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
+humans but algorithmically detectable from a short span of tokens. We propose a
+watermarking framework for proprietary language models. The watermark can be
+embedded with negligible impact on text quality, and can be detected using an
+efficient open-source algorithm without access to the language model API or
+parameters. The watermark works by selecting a randomized set of "green" tokens
+before a word is generated, and then softly promoting use of green tokens
+during sampling. We propose a statistical test for detecting the watermark with
+interpretable p-values, and derive an information-theoretic framework for
+analyzing the sensitivity of the watermark. We test the watermark using a
+multi-billion parameter model from the Open Pretrained Transformer (OPT)
+family, and discuss robustness and security.
+                
+## Precise Zero-Shot Dense Retrieval without Relevance Labels
+
+- **arXiv id:** 2212.10496v1
+- **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 Documentation:** [docs/use_cases/query_analysis/techniques/hyde](https://python.langchain.com/docs/use_cases/query_analysis/techniques/hyde)
+- **LangChain API Reference:** [langchain.chains.hyde.base.HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
+
+**Abstract:** While dense retrieval has been shown effective and efficient across tasks and
+languages, it remains difficult to create effective fully zero-shot dense
+retrieval systems when no relevance label is available. In this paper, we
+recognize the difficulty of zero-shot learning and encoding relevance. Instead,
+we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a
+query, HyDE first zero-shot instructs an instruction-following language model
+(e.g. InstructGPT) to generate a hypothetical document. The document captures
+relevance patterns but is unreal and may contain false details. Then, an
+unsupervised contrastively learned encoder~(e.g. Contriever) encodes the
+document into an embedding vector. This vector identifies a neighborhood in the
+corpus embedding space, where similar real documents are retrieved based on
+vector similarity. This second step ground the generated document to the actual
+corpus, with the encoder's dense bottleneck filtering out the incorrect
+details. Our experiments show that HyDE significantly outperforms the
+state-of-the-art unsupervised dense retriever Contriever and shows strong
+performance comparable to fine-tuned retrievers, across various tasks (e.g. web
+search, QA, fact verification) and languages~(e.g. sw, ko, ja).
+                
+## Constitutional AI: Harmlessness from AI Feedback
+
+- **arXiv id:** 2212.08073v1
+- **Title:** Constitutional AI: Harmlessness from AI Feedback
+- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al.
+- **Published Date:** 2022-12-15
+- **URL:** http://arxiv.org/abs/2212.08073v1
+- **LangChain Documentation:** [docs/guides/productionization/evaluation/string/criteria_eval_chain](https://python.langchain.com/docs/guides/productionization/evaluation/string/criteria_eval_chain)
+
+
+**Abstract:** As AI systems become more capable, we would like to enlist their help to
+supervise other AIs. We experiment with methods for training a harmless AI
+assistant through self-improvement, without any human labels identifying
+harmful outputs. The only human oversight is provided through a list of rules
+or principles, and so we refer to the method as 'Constitutional AI'. The
+process involves both a supervised learning and a reinforcement learning phase.
+In the supervised phase we sample from an initial model, then generate
+self-critiques and revisions, and then finetune the original model on revised
+responses. In the RL phase, we sample from the finetuned model, use a model to
+evaluate which of the two samples is better, and then train a preference model
+from this dataset of AI preferences. We then train with RL using the preference
+model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
+result we are able to train a harmless but non-evasive AI assistant that
+engages with harmful queries by explaining its objections to them. Both the SL
+and RL methods can leverage chain-of-thought style reasoning to improve the
+human-judged performance and transparency of AI decision making. These methods
+make it possible to control AI behavior more precisely and with far fewer human
+labels.
+                
+## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
+
+- **arXiv id:** 2212.07425v3
+- **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)
+
+**Abstract:** The spread of misinformation, propaganda, and flawed argumentation has been
+amplified in the Internet era. Given the volume of data and the subtlety of
+identifying violations of argumentation norms, supporting information analytics
+tasks, like content moderation, with trustworthy methods that can identify
+logical fallacies is essential. In this paper, we formalize prior theoretical
+work on logical fallacies into a comprehensive three-stage evaluation framework
+of detection, coarse-grained, and fine-grained classification. We adapt
+existing evaluation datasets for each stage of the evaluation. We employ three
+families of robust and explainable methods based on prototype reasoning,
+instance-based reasoning, and knowledge injection. The methods combine language
+models with background knowledge and explainable mechanisms. Moreover, we
+address data sparsity with strategies for data augmentation and curriculum
+learning. Our three-stage framework natively consolidates prior datasets and
+methods from existing tasks, like propaganda detection, serving as an
+overarching evaluation testbed. We extensively evaluate these methods on our
+datasets, focusing on their robustness and explainability. Our results provide
+insight into the strengths and weaknesses of the methods on different
+components and fallacy classes, indicating that fallacy identification is a
+challenging task that may require specialized forms of reasoning to capture
+various classes. We share our open-source code and data on GitHub to support
+further work on logical fallacy identification.
+                
+## Complementary Explanations for Effective In-Context Learning
+
+- **arXiv id:** 2211.13892v2
+- **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.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
+
+**Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
+learning from explanations in prompts, but there has been limited understanding
+of exactly how these explanations function or why they are effective. This work
+aims to better understand the mechanisms by which explanations are used for
+in-context learning. We first study the impact of two different factors on the
+performance of prompts with explanations: the computation trace (the way the
+solution is decomposed) and the natural language used to express the prompt. By
+perturbing explanations on three controlled tasks, we show that both factors
+contribute to the effectiveness of explanations. We further study how to form
+maximally effective sets of explanations for solving a given test query. We
+find that LLMs can benefit from the complementarity of the explanation set:
+diverse reasoning skills shown by different exemplars can lead to better
+performance. Therefore, we propose a maximal marginal relevance-based exemplar
+selection approach for constructing exemplar sets that are both relevant as
+well as complementary, which successfully improves the in-context learning
+performance across three real-world tasks on multiple LLMs.
+                
+## PAL: Program-aided Language Models
+
+- **arXiv id:** 2211.10435v2
+- **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.pal_chain.base.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)
+
+**Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
+to perform arithmetic and symbolic reasoning tasks, when provided with a few
+examples at test time ("few-shot prompting"). Much of this success can be
+attributed to prompting methods such as "chain-of-thought'', which employ LLMs
+for both understanding the problem description by decomposing it into steps, as
+well as solving each step of the problem. While LLMs seem to be adept at this
+sort of step-by-step decomposition, LLMs often make logical and arithmetic
+mistakes in the solution part, even when the problem is decomposed correctly.
+In this paper, we present Program-Aided Language models (PAL): a novel approach
+that uses the LLM to read natural language problems and generate programs as
+the intermediate reasoning steps, but offloads the solution step to a runtime
+such as a Python interpreter. With PAL, decomposing the natural language
+problem into runnable steps remains the only learning task for the LLM, while
+solving is delegated to the interpreter. We demonstrate this synergy between a
+neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and
+algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all
+these natural language reasoning tasks, generating code using an LLM and
+reasoning using a Python interpreter leads to more accurate results than much
+larger models. For example, PAL using Codex achieves state-of-the-art few-shot
+accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
+which uses chain-of-thought by absolute 15% top-1. Our code and data are
+publicly available at http://reasonwithpal.com/ .
+                
+## Deep Lake: a Lakehouse for Deep Learning
+
+- **arXiv id:** 2209.10785v2
+- **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)
+
+
+**Abstract:** Traditional data lakes provide critical data infrastructure for analytical
+workloads by enabling time travel, running SQL queries, ingesting data with
+ACID transactions, and visualizing petabyte-scale datasets on cloud storage.
+They allow organizations to break down data silos, unlock data-driven
+decision-making, improve operational efficiency, and reduce costs. However, as
+deep learning usage increases, traditional data lakes are not well-designed for
+applications such as natural language processing (NLP), audio processing,
+computer vision, and applications involving non-tabular datasets. This paper
+presents Deep Lake, an open-source lakehouse for deep learning applications
+developed at Activeloop. Deep Lake maintains the benefits of a vanilla data
+lake with one key difference: it stores complex data, such as images, videos,
+annotations, as well as tabular data, in the form of tensors and rapidly
+streams the data over the network to (a) Tensor Query Language, (b) in-browser
+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.
+                
+## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
+
+- **arXiv id:** 2205.12654v1
+- **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.embeddings.laser.LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
+
+**Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
+languages is challenging, in particular to cover the long tail of low-resource
+languages. A promising approach has been to train one-for-all multilingual
+models capable of cross-lingual transfer, but these models often suffer from
+insufficient capacity and interference between unrelated languages. Instead, we
+move away from this approach and focus on training multiple language (family)
+specific representations, but most prominently enable all languages to still be
+encoded in the same representational space. To achieve this, we focus on
+teacher-student training, allowing all encoders to be mutually compatible for
+bitext mining, and enabling fast learning of new languages. We introduce a new
+teacher-student training scheme which combines supervised and self-supervised
+training, allowing encoders to take advantage of monolingual training data,
+which is valuable in the low-resource setting.
+  Our approach significantly outperforms the original LASER encoder. We study
+very low-resource languages and handle 50 African languages, many of which are
+not covered by any other model. For these languages, we train sentence
+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
+- **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 Documentation:** [docs/use_cases/sql/quickstart](https://python.langchain.com/docs/use_cases/sql/quickstart)
+- **LangChain API Reference:** [langchain_community.utilities.sql_database.SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities.spark_sql.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
+baseline on the Spider benchmark; we also analyze the failure modes of Codex in
+this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
+benchmarks that a small number of in-domain examples provided in the prompt
+enables Codex to perform better than state-of-the-art models finetuned on such
+few-shot examples.
+                
+## Locally Typical Sampling
+
+- **arXiv id:** 2202.00666v5
+- **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.llms.huggingface_text_gen_inference.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), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+
+**Abstract:** Today's probabilistic language generators fall short when it comes to
+producing coherent and fluent text despite the fact that the underlying models
+perform well under standard metrics, e.g., perplexity. This discrepancy has
+puzzled the language generation community for the last few years. In this work,
+we posit that the abstraction of natural language generation as a discrete
+stochastic process--which allows for an information-theoretic analysis--can
+provide new insights into the behavior of probabilistic language generators,
+e.g., why high-probability texts can be dull or repetitive. Humans use language
+as a means of communicating information, aiming to do so in a simultaneously
+efficient and error-minimizing manner; in fact, psycholinguistics research
+suggests humans choose each word in a string with this subconscious goal in
+mind. We formally define the set of strings that meet this criterion: those for
+which each word has an information content close to the expected information
+content, i.e., the conditional entropy of our model. We then propose a simple
+and efficient procedure for enforcing this criterion when generating from
+probabilistic models, which we call locally typical sampling. Automatic and
+human evaluations show that, in comparison to nucleus and top-k sampling,
+locally typical sampling offers competitive performance (in both abstractive
+summarization and story generation) in terms of quality while consistently
+reducing degenerate repetitions.
+                
+## Learning Transferable Visual Models From Natural Language Supervision
+
+- **arXiv id:** 2103.00020v1
+- **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)
+
+**Abstract:** State-of-the-art computer vision systems are trained to predict a fixed set
+of predetermined object categories. This restricted form of supervision limits
+their generality and usability since additional labeled data is needed to
+specify any other visual concept. Learning directly from raw text about images
+is a promising alternative which leverages a much broader source of
+supervision. We demonstrate that the simple pre-training task of predicting
+which caption goes with which image is an efficient and scalable way to learn
+SOTA image representations from scratch on a dataset of 400 million (image,
+text) pairs collected from the internet. After pre-training, natural language
+is used to reference learned visual concepts (or describe new ones) enabling
+zero-shot transfer of the model to downstream tasks. We study the performance
+of this approach by benchmarking on over 30 different existing computer vision
+datasets, spanning tasks such as OCR, action recognition in videos,
+geo-localization, and many types of fine-grained object classification. The
+model transfers non-trivially to most tasks and is often competitive with a
+fully supervised baseline without the need for any dataset specific training.
+For instance, we match the accuracy of the original ResNet-50 on ImageNet
+zero-shot without needing to use any of the 1.28 million training examples it
+was trained on. We release our code and pre-trained model weights at
+https://github.com/OpenAI/CLIP.
+                
+## CTRL: A Conditional Transformer Language Model for Controllable Generation
+
+- **arXiv id:** 1909.05858v2
+- **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.llms.huggingface_text_gen_inference.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), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
+
+**Abstract:** Large-scale language models show promising text generation capabilities, but
+users cannot easily control particular aspects of the generated text. We
+release CTRL, a 1.63 billion-parameter conditional transformer language model,
+trained to condition on control codes that govern style, content, and
+task-specific behavior. Control codes were derived from structure that
+naturally co-occurs with raw text, preserving the advantages of unsupervised
+learning while providing more explicit control over text generation. These
+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/docusaurus.config.js b/docs/docusaurus.config.js
index a9ef56bb35..2d0e793e8c 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -202,6 +202,10 @@ const config = {
                 docId: "additional_resources/youtube",
                 label: "YouTube"
               },
+              {
+                to: "/docs/additional_resources/arxiv_references",
+                label: "arXiv"
+              },
             ]
           },
           {
diff --git a/docs/scripts/arxiv_references.py b/docs/scripts/arxiv_references.py
new file mode 100644
index 0000000000..55d9169d83
--- /dev/null
+++ b/docs/scripts/arxiv_references.py
@@ -0,0 +1,465 @@
+"""Parse arXiv references from the documentation.
+Generate a page with a table of the arXiv references with links to the documentation pages.
+"""
+
+import logging
+import os
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Set
+
+from pydantic.v1 import BaseModel, root_validator
+
+# TODO parse docstrings for arXiv references
+# TODO Generate a page with a table of the references with correspondent modules/classes/functions.
+
+logger = logging.getLogger(__name__)
+
+_ROOT_DIR = Path(os.path.abspath(__file__)).parents[2]
+DOCS_DIR = _ROOT_DIR / "docs" / "docs"
+CODE_DIR = _ROOT_DIR / "libs"
+ARXIV_ID_PATTERN = r"https://arxiv\.org/(abs|pdf)/(\d+\.\d+)"
+
+
+@dataclass
+class ArxivPaper:
+    """ArXiv paper information."""
+
+    arxiv_id: str
+    referencing_docs: list[str]  # TODO: Add the referencing docs
+    referencing_api_refs: list[str]  # TODO: Add the referencing docs
+    title: str
+    authors: list[str]
+    abstract: str
+    url: str
+    published_date: str
+
+
+def search_documentation_for_arxiv_references(docs_dir: Path) -> dict[str, set[str]]:
+    """Search the documentation for arXiv references.
+
+    Search for the arXiv references in the documentation pages.
+    Note: It finds only the first arXiv reference in a line.
+
+    Args:
+        docs_dir: Path to the documentation root folder.
+    Returns:
+        dict: Dictionary with arxiv_id as key and set of file names as value.
+    """
+    arxiv_url_pattern = re.compile(ARXIV_ID_PATTERN)
+    exclude_strings = {"file_path", "metadata", "link", "loader", "PyPDFLoader"}
+
+    # loop all the files (ipynb, mdx, md) in the docs folder
+    files = (
+        p.resolve()
+        for p in Path(docs_dir).glob("**/*")
+        if p.suffix in {".ipynb", ".mdx", ".md"}
+    )
+    arxiv_id2file_names: dict[str, set[str]] = {}
+    for file in files:
+        if "-checkpoint.ipynb" in file.name:
+            continue
+        with open(file, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+            for line in lines:
+                if any(exclude_string in line for exclude_string in exclude_strings):
+                    continue
+                matches = arxiv_url_pattern.search(line)
+                if matches:
+                    arxiv_id = matches.group(2)
+                    file_name = _get_doc_path(file.parts, file.suffix)
+                    if arxiv_id not in arxiv_id2file_names:
+                        arxiv_id2file_names[arxiv_id] = {file_name}
+                    else:
+                        arxiv_id2file_names[arxiv_id].add(file_name)
+    return arxiv_id2file_names
+
+
+def convert_module_name_and_members_to_urls(
+    arxiv_id2module_name_and_members: dict[str, set[str]],
+) -> dict[str, set[str]]:
+    arxiv_id2urls = {}
+    for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
+        urls = set()
+        for module_name_and_member in module_name_and_members:
+            module_name, type_and_member = module_name_and_member.split(":")
+            if "$" in type_and_member:
+                type, member = type_and_member.split("$")
+            else:
+                type = type_and_member
+                member = ""
+            _namespace_parts = module_name.split(".")
+            if type == "module":
+                first_namespace_part = _namespace_parts[0]
+                if first_namespace_part.startswith("langchain_"):
+                    first_namespace_part = first_namespace_part.replace(
+                        "langchain_", ""
+                    )
+                url = f"{first_namespace_part}_api_reference.html#module-{module_name}"
+            elif type in ["class", "function"]:
+                second_namespace_part = _namespace_parts[1]
+                url = f"{second_namespace_part}/{module_name}.{member}.html#{module_name}.{member}"
+            else:
+                raise ValueError(
+                    f"Unknown type: {type} in the {module_name_and_member}."
+                )
+            urls.add(url)
+        arxiv_id2urls[arxiv_id] = urls
+    return arxiv_id2urls
+
+
+def search_code_for_arxiv_references(code_dir: Path) -> dict[str, set[str]]:
+    """Search the code for arXiv references.
+
+    Search for the arXiv references in the code.
+    Note: It finds only the first arXiv reference in a line.
+
+    Args:
+        code_dir: Path to the code root folder.
+    Returns:
+        dict: Dictionary with arxiv_id as key and set of module names as value.
+          module names encoded as:
+            <module_name>:module
+            <module_name>:class$<ClassName>
+            <module_name>:function$<function_name>
+    """
+    arxiv_url_pattern = re.compile(ARXIV_ID_PATTERN)
+    # exclude_strings = {"file_path", "metadata", "link", "loader"}
+    class_pattern = re.compile(r"\s*class\s+(\w+).*:")
+    function_pattern = re.compile(r"\s*def\s+(\w+)")
+
+    # loop all the files (ipynb, mdx, md) in the docs folder
+    files = (
+        p.resolve()
+        for p in Path(code_dir).glob("**/*")
+        if p.suffix in {".py"} and "tests" not in p.parts and "scripts" not in p.parts
+        # ".md" files are excluded
+    )
+    arxiv_id2module_name_and_members: dict[str, set[str]] = {}
+    for file in files:
+        try:
+            with open(file, "r", encoding="utf-8") as f:
+                module_name = _get_module_name(file.parts)
+                class_or_function_started = "module"
+                for line in f.readlines():
+                    # class line:
+                    matches = class_pattern.search(line)
+                    if matches:
+                        class_name = matches.group(1)
+                        class_or_function_started = f"class${class_name}"
+
+                    # function line:
+                    #  not inside a class!
+                    if "class" not in class_or_function_started:
+                        matches = function_pattern.search(line)
+                        if matches:
+                            func_name = matches.group(1)
+                            class_or_function_started = f"function${func_name}"
+
+                    # arxiv line:
+                    matches = arxiv_url_pattern.search(line)
+                    if matches:
+                        arxiv_id = matches.group(2)
+                        module_name_and_member = (
+                            f"{module_name}:{class_or_function_started}"
+                        )
+                        if arxiv_id not in arxiv_id2module_name_and_members:
+                            arxiv_id2module_name_and_members[arxiv_id] = {
+                                module_name_and_member
+                            }
+                        else:
+                            arxiv_id2module_name_and_members[arxiv_id].add(
+                                module_name_and_member
+                            )
+        except UnicodeDecodeError:
+            # Skip files like this 'tests/integration_tests/examples/non-utf8-encoding.py'
+            logger.warning(f"Could not read the file {file}.")
+
+    # handle border cases:
+    # 1. {'langchain_experimental.pal_chain.base:class$PALChain', 'langchain_experimental.pal_chain.base:module' - remove}
+    for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
+        module_name_and_member_deduplicated = set()
+        non_module_members = set()
+        for module_name_and_member in module_name_and_members:
+            if not module_name_and_member.endswith(":module"):
+                module_name_and_member_deduplicated.add(module_name_and_member)
+                non_module_members.add(module_name_and_member.split(":")[0])
+        for module_name_and_member in module_name_and_members:
+            if module_name_and_member.endswith(":module"):
+                if module_name_and_member.split(":")[0] in non_module_members:
+                    continue
+                module_name_and_member_deduplicated.add(module_name_and_member)
+        arxiv_id2module_name_and_members[arxiv_id] = module_name_and_member_deduplicated
+
+    # 2. {'langchain.evaluation.scoring.prompt:module', 'langchain.evaluation.comparison.prompt:module'}
+    #    only modules with 2-part namespaces are parsed into API Reference now! TODO fix this behavior
+    #    leave only the modules with 2-part namespaces
+    arxiv_id2module_name_and_members_reduced = {}
+    for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
+        module_name_and_member_reduced = set()
+        removed_modules = set()
+        for module_name_and_member in module_name_and_members:
+            if module_name_and_member.endswith(":module"):
+                if module_name_and_member.split(":")[0].count(".") <= 1:
+                    module_name_and_member_reduced.add(module_name_and_member)
+                else:
+                    removed_modules.add(module_name_and_member)
+            else:
+                module_name_and_member_reduced.add(module_name_and_member)
+        if module_name_and_member_reduced:
+            arxiv_id2module_name_and_members_reduced[arxiv_id] = (
+                module_name_and_member_reduced
+            )
+        if removed_modules:
+            logger.warning(
+                f"{arxiv_id}: Removed the following modules with 2+ -part namespaces: {removed_modules}."
+            )
+    return arxiv_id2module_name_and_members_reduced
+
+
+def _get_doc_path(file_parts: tuple[str, ...], file_extension) -> str:
+    """Get the relative path to the documentation page
+    from the absolute path of the file.
+    Remove file_extension
+    """
+    res = []
+    for el in file_parts[::-1]:
+        res.append(el)
+        if el == "docs":
+            break
+    ret = "/".join(reversed(res))
+    return ret[: -len(file_extension)] if ret.endswith(file_extension) else ret
+
+
+def _get_code_path(file_parts: tuple[str, ...]) -> str:
+    """Get the relative path to the documentation page
+    from the absolute path of the file.
+    """
+    res = []
+    for el in file_parts[::-1]:
+        res.append(el)
+        if el == "libs":
+            break
+    return "/".join(reversed(res))
+
+
+def _get_module_name(file_parts: tuple[str, ...]) -> str:
+    """Get the module name from the absolute path of the file."""
+    ns_parts = []
+    for el in file_parts[::-1]:
+        if str(el) == "__init__.py":
+            continue
+        ns_parts.insert(0, str(el).replace(".py", ""))
+        if el.startswith("langchain"):
+            break
+    return ".".join(ns_parts)
+
+
+def compound_urls(
+    arxiv_id2file_names: dict[str, set[str]], arxiv_id2code_urls: dict[str, set[str]]
+) -> dict[str, dict[str, set[str]]]:
+    arxiv_id2urls = dict()
+    for arxiv_id, code_urls in arxiv_id2code_urls.items():
+        arxiv_id2urls[arxiv_id] = {"api": code_urls}
+        # intersection of the two sets
+        if arxiv_id in arxiv_id2file_names:
+            arxiv_id2urls[arxiv_id]["docs"] = arxiv_id2file_names[arxiv_id]
+    for arxiv_id, file_names in arxiv_id2file_names.items():
+        if arxiv_id not in arxiv_id2code_urls:
+            arxiv_id2urls[arxiv_id] = {"docs": file_names}
+    # reverse sort by the arxiv_id (the newest papers first)
+    ret = dict(sorted(arxiv_id2urls.items(), key=lambda item: item[0], reverse=True))
+    return ret
+
+
+def _format_doc_link(doc_paths: list[str]) -> list[str]:
+    return [
+        f"[{doc_path}](https://python.langchain.com/{doc_path})"
+        for doc_path in doc_paths
+    ]
+
+
+def _format_api_ref_link(
+    doc_paths: list[str], compact: bool = False
+) -> list[str]:  # TODO
+    # agents/langchain_core.agents.AgentAction.html#langchain_core.agents.AgentAction
+    ret = []
+    for doc_path in doc_paths:
+        module = doc_path.split("#")[1].replace("module-", "")
+        if compact and module.count(".") > 2:
+            # langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI
+            # -> langchain_community.llms...OCIModelDeploymentTGI
+            module_parts = module.split(".")
+            module = f"{module_parts[0]}.{module_parts[1]}...{module_parts[-1]}"
+        ret.append(
+            f"[{module}](https://api.python.langchain.com/en/latest/{doc_path.split('langchain.com/')[-1]})"
+        )
+    return ret
+
+
+def log_results(arxiv_id2urls):
+    arxiv_ids = arxiv_id2urls.keys()
+    doc_number, api_number = 0, 0
+    for urls in arxiv_id2urls.values():
+        if "docs" in urls:
+            doc_number += len(urls["docs"])
+        if "api" in urls:
+            api_number += len(urls["api"])
+    logger.info(
+        f"Found {len(arxiv_ids)} arXiv references in the {doc_number} docs and in {api_number} API Refs."
+    )
+
+
+class ArxivAPIWrapper(BaseModel):
+    arxiv_search: Any  #: :meta private:
+    arxiv_exceptions: Any  # :meta private:
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+        try:
+            import arxiv
+
+            values["arxiv_search"] = arxiv.Search
+            values["arxiv_exceptions"] = (
+                arxiv.ArxivError,
+                arxiv.UnexpectedEmptyPageError,
+                arxiv.HTTPError,
+            )
+        except ImportError:
+            raise ImportError(
+                "Could not import arxiv python package. "
+                "Please install it with `pip install arxiv`."
+            )
+        return values
+
+    def get_papers(
+        self, arxiv_id2urls: dict[str, dict[str, set[str]]]
+    ) -> list[ArxivPaper]:
+        """
+        Performs an arxiv search and returns information about the papers found.
+
+        If an error occurs or no documents found, error text
+        is returned instead.
+        Args:
+            arxiv_id2urls: Dictionary with arxiv_id as key and dictionary
+             with sets of doc file names and API Ref urls.
+
+        Returns:
+            List of ArxivPaper objects.
+        """  # noqa: E501
+
+        def cut_authors(authors: list) -> list[str]:
+            if len(authors) > 3:
+                return [str(a) for a in authors[:3]] + [" et al."]
+            else:
+                return [str(a) for a in authors]
+
+        if not arxiv_id2urls:
+            return []
+        try:
+            arxiv_ids = list(arxiv_id2urls.keys())
+            results = self.arxiv_search(
+                id_list=arxiv_ids,
+                max_results=len(arxiv_ids),
+            ).results()
+        except self.arxiv_exceptions as ex:
+            raise ex
+        papers = [
+            ArxivPaper(
+                arxiv_id=result.entry_id.split("/")[-1],
+                title=result.title,
+                authors=cut_authors(result.authors),
+                abstract=result.summary,
+                url=result.entry_id,
+                published_date=str(result.published.date()),
+                referencing_docs=urls["docs"] if "docs" in urls else [],
+                referencing_api_refs=urls["api"] if "api" in urls else [],
+            )
+            for result, urls in zip(results, arxiv_id2urls.values())
+        ]
+        return papers
+
+
+def generate_arxiv_references_page(file_name: str, papers: list[ArxivPaper]) -> None:
+    with open(file_name, "w") as f:
+        # Write the table headers
+        f.write("""# arXiv
+            
+LangChain implements the latest research in the field of Natural Language Processing.
+This page contains `arXiv` papers referenced in the LangChain Documentation and API Reference.
+
+## Summary
+
+| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation and API Reference |
+|------------------|---------|-------------------|-------------------------|
+""")
+        for paper in papers:
+            refs = []
+            if paper.referencing_docs:
+                refs += [
+                    "`Docs:` " + ", ".join(_format_doc_link(paper.referencing_docs))
+                ]
+            if paper.referencing_api_refs:
+                refs += [
+                    "`API:` "
+                    + ", ".join(
+                        _format_api_ref_link(paper.referencing_api_refs, compact=True)
+                    )
+                ]
+            refs_str = ", ".join(refs)
+
+            title_link = f"[{paper.title}]({paper.url})"
+            f.write(
+                f"| {' | '.join([f'`{paper.arxiv_id}` {title_link}', ', '.join(paper.authors), paper.published_date, refs_str])}\n"
+            )
+
+        for paper in papers:
+            docs_refs = (
+                f"- **LangChain Documentation:** {', '.join(_format_doc_link(paper.referencing_docs))}"
+                if paper.referencing_docs
+                else ""
+            )
+            api_ref_refs = (
+                f"- **LangChain API Reference:** {', '.join(_format_api_ref_link(paper.referencing_api_refs))}"
+                if paper.referencing_api_refs
+                else ""
+            )
+            f.write(f"""
+## {paper.title}
+
+- **arXiv id:** {paper.arxiv_id}
+- **Title:** {paper.title}
+- **Authors:** {', '.join(paper.authors)}
+- **Published Date:** {paper.published_date}
+- **URL:** {paper.url}
+{docs_refs}
+{api_ref_refs}
+
+**Abstract:** {paper.abstract}
+                """)
+
+    logger.info(f"Created the {file_name} file with {len(papers)} arXiv references.")
+
+
+def main():
+    # search the documentation and the API Reference for arXiv references:
+    arxiv_id2module_name_and_members = search_code_for_arxiv_references(CODE_DIR)
+    arxiv_id2code_urls = convert_module_name_and_members_to_urls(
+        arxiv_id2module_name_and_members
+    )
+    arxiv_id2file_names = search_documentation_for_arxiv_references(DOCS_DIR)
+    arxiv_id2urls = compound_urls(arxiv_id2file_names, arxiv_id2code_urls)
+    log_results(arxiv_id2urls)
+
+    # get the arXiv paper information
+    papers = ArxivAPIWrapper().get_papers(arxiv_id2urls)
+
+    # generate the arXiv references page
+    output_file = str(DOCS_DIR / "additional_resources" / "arxiv_references.mdx")
+    generate_arxiv_references_page(output_file, papers)
+
+
+if __name__ == "__main__":
+    main()