This is the collection of papers related to large language models for information retrieval. These papers are organized according to our survey paper Large Language Models for Information Retrieval: A Survey.
Feel free to contact us if you find a mistake or have any advice. Email: yutaozhu94@gmail.com and dou@ruc.edu.cn.
Please kindly cite our paper if helps your research:
@article{LLM4IRSurvey,
author={Yutao Zhu and
Huaying Yuan and
Shuting Wang and
Jiongnan Liu and
Wenhan Liu and
Chenlong Deng and
Haonan Chen and
Zhicheng Dou and
Ji-Rong Wen},
title={Large Language Models for Information Retrieval: A Survey},
journal={CoRR},
volume={abs/2308.07107},
year={2023},
url={https://arxiv.org/abs/2308.07107},
eprinttype={arXiv},
eprint={2308.07107}
}
- Version 2 [2024-01-19]
- We added a new section to introduce search agents, which represent an innovative approach to integrating LLMs with IR systems.
- Rewriter: We added recent works on LLM-based query rewriting, most of which focus on conversational search.
- Retriever: We added the latest techniques that leverage LLMs to expand the training corpus for retrievers or to enhance retrievers' architectures.
- Reranker: We added recent LLM-based ranking works to each of the three part: Utilizing LLMs as Supervised Rerankers, Utilizing LLMs as Unsupervised Rerankers, and Utilizing LLMs for Training Data Augmentation.
- Reader: We added the latest studies in LLM-enhanced reader area, including a section introducing the reference compression technique, a section discussing the applications of LLM-enhanced readers, and a section analyzing the characteristics of LLM-enhanced readers.
- Future Direction: We added a section about search agents and a section discussing the bias caused by leveraging LLMs into IR systems.
- Query2doc: Query Expansion with Large Language Models, Wang et al., arXiv 2023. [Paper]
- Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval, Mackie et al., arXiv 2023. [Paper]
- Generative Relevance Feedback with Large Language Models, Mackie et al., SIGIR 2023 (short paper). [Paper]
- GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval, Mackie et al., arXiv 2023. [Paper]
- Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search, Mao et al., arXiv 2023. [Paper]
- Precise Zero-Shot Dense Retrieval without Relevance Labels, Gao et al., ACL 2023. [Paper]
- Query Expansion by Prompting Large Language Models, Jagerman et al., arXiv 2023. [Paper]
- Large Language Models are Strong Zero-Shot Retriever, Shen et al., arXiv 2023. [Paper]
- Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting, Ye et al., EMNLP 2023 (Findings). [Paper]
- Can generative llms create query variants for test collections? an exploratory study, M. Alaofi et al., SIGIR 2023 (short paper). [Paper]
- Corpus-Steered Query Expansion with Large Language Models, Lei et al., EACL 2024 (Short Paper). [Paper]
- PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval, Zhuang et al., arXiv 2024. [Paper]
- QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation, Srinivasan et al., EMNLP 2022 (Industry). [Paper] (This paper explore fine-tuning methods in baseline experiments.)
- QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation, Srinivasan et al., EMNLP 2022 (Industry). [Paper]
- Knowledge Refinement via Interaction Between Search Engines and Large Language Models, Feng et al., arXiv 2023. [Paper]
- Query Rewriting for Retrieval-Augmented Large Language Models, Ma et al., arXiv 2023. [Paper]
- InPars: Data Augmentation for Information Retrieval using Large Language Models, Bonifacio et al., arXiv 2022. [Paper]
- Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval, Ma et al., arXiv 2023. [Paper]
- InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval, Jeronymo et al., arXiv 2023. [Paper]
- Promptagator: Few-shot Dense Retrieval From 8 Examples, Dai et al., ICLR 2023. [Paper]
- AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation, Meng et al., arXiv 2023. [Paper]
- UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers, Saad-Falco et al., arXiv 2023. [Paper]
- Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models, Peng et al., arXiv 2023. [Paper]
- CONVERSER: Few-shot Conversational Dense Retrieval with Synthetic Data Generation, Huang et al., ACL 2023. [Paper]
- Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval, Thakur et al., arXiv 2023. [Paper]
- Questions Are All You Need to Train a Dense Passage Retriever, Sachan et al., ACL 2023. [Paper]
- Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators, Chen et al., EMNLP 2023. [Paper]
- Text and Code Embeddings by Contrastive Pre-Training, Neelakantan et al., arXiv 2022. [Paper]
- Fine-Tuning LLaMA for Multi-Stage Text Retrieval, Ma et al., arXiv 2023. [Paper]
- Large Dual Encoders Are Generalizable Retrievers, Ni et al., EMNLP 2022. [Paper]
- Task-aware Retrieval with Instructions, Asai et al., ACL 2023 (Findings). [Paper]
- Transformer memory as a differentiable search index, Tay et al., NeurIPS 2022. [Paper]
- Large Language Models are Built-in Autoregressive Search Engines, Ziems et al., ACL 2023 (Findings). [Paper]
- Multi-Stage Document Ranking with BERT, Nogueira et al., arXiv 2019. [Paper]
- Document Ranking with a Pretrained Sequence-to-Sequence Model, Nogueira et al., EMNLP 2020 (Findings). [Paper]
- Text-to-Text Multi-view Learning for Passage Re-ranking, Ju et al., SIGIR 2021 (Short Paper). [Paper]
- The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models, Pradeep et al., arXiv 2021. [Paper]
- RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses, Zhuang et al., SIGIR 2023 (Short Paper). [Paper]
- Fine-Tuning LLaMA for Multi-Stage Text Retrieval, Ma et al., arXiv 2023. [Paper]
- RankingGPT: Empowering Large Language Models in Text Ranking with Progressive Enhancement, Zhang et al., arXiv 2023. [Paper]
- Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models, Zhang et al., arXiv 2023. [Paper]
- Holistic Evaluation of Language Models, Liang et al., arXiv 2022. [Paper]
- Improving Passage Retrieval with Zero-Shot Question Generation, Sachan et al., EMNLP 2022. [Paper]
- Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker, Cho et al., ACL 2023 (Findings). [Paper]
- Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking, Zhuang et al., EMNLP 2023 (Findings). [Paper]
- PaRaDe: Passage Ranking using Demonstrations with Large Language Models, Drozdov et al., EMNLP 2023 (Findings). [Paper]
- Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels, Zhuang et al., arXiv 2023. [Paper]
- Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent, Sun et al., EMNLP 2023. [Paper]
- Zero-Shot Listwise Document Reranking with a Large Language Model, Ma et al., arXiv 2023. [Paper]
- Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models, Tang et al., arXiv 2023. [Paper]
- Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting, Qin et al., NAACL 2024 (Findings). [Paper]
- A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models, Zhuang et al., SIGIR 2024. [Paper]
- InstUPR: Instruction-based Unsupervised Passage Reranking with Large Language Models, Huang and Chen, arXiv 2024. [Paper]
- ExaRanker: Explanation-Augmented Neural Ranker, Ferraretto et al., SIGIR 2023 (Short Paper). [Paper]
- InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers, Boytsov et al., arXiv 2023. [Paper]
- Generating Synthetic Documents for Cross-Encoder Re-Rankers, Askari et al., arXiv 2023. [Paper]
- Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers, Sun et al., arXiv 2023. [Paper]
- RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models, Pradeep et al., arXiv 2023. [Paper]
- RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!, Pradeep et al., arXiv 2023. [Paper]
- REALM: Retrieval-Augmented Language Model Pre-Training, Guu et al., ICML 2020. [Paper]
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Lewis et al., NeurIPS 2020. [Paper]
- REPLUG: Retrieval-Augmented Black-Box Language Models, Shi et al., arXiv 2023. [Paper]
- Atlas: Few-shot Learning with Retrieval Augmented Language Models, Izacard et al., JMLR 2023. [Paper]
- Internet-augmented Language Models through Few-shot Prompting for Open-domain Question Answering, Lazaridou et al., arXiv 2022. [Paper]
- Rethinking with Retrieval: Faithful Large Language Model Inference, He et al., arXiv 2023. [Paper]
- FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation, Vu et al., arxiv 2023. [Paper]
- Enabling Large Language Models to Generate Text with Citations, Gao et al., EMNLP 2023. [Paper]
- Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models, Yu et al., arxiv 2023. [Paper]
- Improving Retrieval-Augmented Large Language Models via Data Importance Learning, Lyu et al., arXiv 2023. [Paper]
- Search Augmented Instruction Learning, Luo et al., EMNLP 2023 (Findings). [Paper]
- RADIT: Retrieval-Augmented Dual Instruction Tuning, Lin et al., arXiv 2023. [Paper]
- Improving Language Models by Retrieving from Trillions of Tokens, Borgeaud et al., ICML 2022. [Paper]
- In-Context Retrieval-Augmented Language Models, Ram et al., arXiv 2023. [Paper]
- Interleaving Retrieval with Chain-of-thought Reasoning for Knowledge-intensive Multi-step Questions, Trivedi et al., ACL 2023. [Paper]
- Improving Language Models via Plug-and-Play Retrieval Feedback, Yu et al., arXiv 2023. [Paper]
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, Shao et al., EMNLP 2023 (Findings). [Paper]
- Retrieval-Generation Synergy Augmented Large Language Models, Feng et al., arXiv 2023. [Paper]
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, Asai et al., arXiv 2023. [Paper]
- Active Retrieval Augmented Generation, Jiang et al., EMNLP 2023. [Paper]
- Measuring and Narrowing the Compositionality Gap in Language Models, Press et al., arXiv 2022. [Paper]
- DEMONSTRATE–SEARCH–PREDICT: Composing Retrieval and Language Models for Knowledge-intensive NLP, Khattab et al., arXiv 2022. [Paper]
- Answering Questions by Meta-Reasoning over Multiple Chains of Thought, Yoran et al., arXiv 2023. [Paper]
- LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs, Arefeen et al., arXiv 2023. [Paper]
- RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation, Xu et al., arXiv 2023. [Paper]
- TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction, Liu et al., EMNLP 2023 (Findings). [Paper]
- Learning to Filter Context for Retrieval-Augmented Generation, Wang et al., arXiv 2023. [Paper]
- Lost in the Middle: How Language Models Use Long Contexts, Liu et al., arXiv 2023. [Paper]
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation, Ren et al., arXiv 2023. [Paper]
- Exploring the Integration Strategies of Retriever and Large Language Models, Liu et al., arXiv 2023. [Paper]
- Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models, Aksitov et al., arXiv 2023. [Paper]
- When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories, Mallen et al., ACL 2023. [Paper]
- Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering, Wang et al., arXiv 2023. [Paper]
- ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science, Munikoti et al., arXiv 2023. [Paper]
- Crosslingual Retrieval Augmented In-context Learning for Bangla, Li et al., arXiv 2023. [Paper]
- Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature, Lozano et al., arXiv 2023. [Paper]
- Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models, Zhang et al., ICAIF 2023. [Paper]
- Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models, Louis et al., arXiv 2023. [Paper]
- RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit, Liu et al., arXiv 2023. [Paper]
- Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models, Jiang et al., arXiv 2023. [Paper]
- RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models, Hoshi et al., EMNLP 2023. [Paper]
- Don't forget private retrieval: distributed private similarity search for large language models, Zyskind et al., arXiv 2023. [Paper]
- LaMDA: Language Models for Dialog Applications, Thoppilan et al., arXiv 2022. [Paper]
- Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion, Shuster et al., EMNLP 2022 (Findings). [Paper]
- Teaching language models to support answers with verified quotes, Menick et al., arXiv 2022. [Paper]
- WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences, Liu et al., KDD 2023. [Paper]
- A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, Gur et al., arXiv 2023. [Paper]
- Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher, Shi et al., arXiv 2023. [Paper]
- WebGPT: Browser-assisted question-answering with human feedback, Nakano et al., arXiv 2021. [Paper]
- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents, Yao et al., arXiv 2022. [Paper]
- WebCPM: Interactive Web Search for Chinese Long-form Question Answering, Qin et al., ACL 2023. [Paper]
- Mind2Web: Towards a Generalist Agent for the Web, Deng et al., arXiv 2023. [Paper]
- WebArena: A Realistic Web Environment for Building Autonomous Agents, Zhou et al., arXiv 2023. [Paper]
- Hierarchical Prompting Assists Large Language Model on Web Navigation, Sridhar et al., EMNLP 2023 (Findings). [Paper]