来源：LLM4IR-Survey

论文列表

1.查询重写器

1.1提示方法

1. Query2doc: Query Expansion with Large Language Models, Wang et al., arXiv 2023. [Paper]
   利用大型语言模型进行查询扩展。
2. Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval, Mackie et al., arXiv 2023. [Paper]
   针对稀疏、密集和学习到的稀疏检索的生成式和伪相关反馈。
3. Generative Relevance Feedback with Large Language Models, Mackie et al., SIGIR 2023 (short paper). [Paper]
   利用大型语言模型进行生成式相关性反馈。
4. GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval, Mackie et al., arXiv 2023. [Paper]
   GRM：使用相关性感知样本估计的生成式相关性建模，用于文档检索。
5. Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search, Mao et al., arXiv 2023. [Paper]
   大型语言模型了解您的上下文搜索意图：面向对话搜索的提示框架。
6. Precise Zero-Shot Dense Retrieval without Relevance Labels, Gao et al., ACL 2023. [Paper]
   无需相关性标签的精确零样本密集检索。
7. Query Expansion by Prompting Large Language Models, Jagerman et al., arXiv 2023. [Paper]
   通过提示大型语言模型进行查询扩展。
8. Large Language Models are Strong Zero-Shot Retriever, Shen et al., arXiv 2023. [Paper]
   大型语言模型是强大的零样本检索器。
9. Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting, Ye et al., EMNLP 2023 (Findings). [Paper]
   增强对话搜索：大型语言模型辅助的信息查询重写。
10. Can generative llms create query variants for test collections? an exploratory study, M. Alaofi et al., SIGIR 2023 (short paper). [Paper]
    生成式大型语言模型能否为测试集创建查询变体？一项探索性研究。
11. Corpus-Steered Query Expansion with Large Language Models, Lei et al., EACL 2024 (Short Paper). [Paper]
    基于语料库引导的大型语言模型查询扩展。
12. Large language model based long-tail query rewriting in taobao search, Peng et al., WWW 2024. [Paper]
    基于大型语言模型的淘宝搜索长尾查询重写。
13. Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?, Li et al., SIGIR 2024. [Paper]
    查询扩展能否提高强交叉编码器排序器的泛化能力？
14. Query Performance Prediction using Relevance Judgments Generated by Large Language Models, Meng et al., arXiv 2024. [Paper]
    利用大型语言模型生成的相关性判断进行查询性能预测
15. RaFe: Ranking Feedback Improves Query Rewriting for RAG, Mao et al., arXiv 2024. [Paper]
    RaFe：排序反馈改进了RAG的查询重写
16. Crafting the Path: Robust Query Rewriting for Information Retrieval, Baek et al., arXiv 2024. [Paper]
    打造路径：信息检索中的稳健查询重写
17. Query Rewriting for Retrieval-Augmented Large Language Models, Ma et al., arXiv 2023. [Paper]
    检索增强型大型语言模型的查询重写

1.2微调方法

1. 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.)
   利用检索增强和多阶段蒸馏的大型语言模型查询意图

1.3知识蒸馏方法

1. QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation, Srinivasan et al., EMNLP 2022 (Industry). [Paper]
   利用检索增强和多阶段蒸馏的大型语言模型查询意图
2. Knowledge Refinement via Interaction Between Search Engines and Large Language Models, Feng et al., arXiv 2023. [Paper]
   通过搜索引擎与大型语言模型之间的交互进行知识精炼。
3. Query Rewriting for Retrieval-Augmented Large Language Models, Ma et al., arXiv 2023. [Paper]
   面向检索增强的大型语言模型查询重写

2.检索器

2.1利用LLMs生成搜索数据

1. InPars: Data Augmentation for Information Retrieval using Large Language Models, Bonifacio et al., arXiv 2022. [Paper]
   InPars: 使用大型语言模型进行信息检索的数据增强
2. Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval, Ma et al., arXiv 2023. [Paper]
   基于大型语言模型文档扩展的密集段落检索预训练
3. InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval, Jeronymo et al., arXiv 2023. [Paper]
   InPars-v2: 大型语言模型作为信息检索的高效数据集生成器
4. Promptagator: Few-shot Dense Retrieval From 8 Examples, Dai et al., ICLR 2023. [Paper]
   Promptagator: 从8个示例中进行少样本密集检索
5. AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation, Meng et al., arXiv 2023. [Paper]
   AugTriever: 通过可扩展数据增强进行无监督密集检索
6. UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers, Saad-Falco et al., arXiv 2023. [Paper]
   UDAPDR: 通过LLM提示和重排器蒸馏进行无监督领域适应
7. Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models, Peng et al., arXiv 2023. [Paper]
   软提示调优以增强大型语言模型的密集检索
8. CONVERSER: Few-shot Conversational Dense Retrieval with Synthetic Data Generation, Huang et al., ACL 2023. [Paper]
   CONVERSER: 使用合成数据生成进行少样本对话密集检索
9. Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval, Thakur et al., arXiv 2023. [Paper]
   利用LLMs为多语言密集检索合成多语言训练数据
10. Questions Are All You Need to Train a Dense Passage Retriever, Sachan et al., ACL 2023. [Paper]
    问题是你训练密集段落检索器所需的一切
11. Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators, Chen et al., EMNLP 2023. [Paper]
    超越事实性: 大型语言模型作为知识生成器的全面评估
12. Gecko: Versatile Text Embeddings Distilled from Large Language Models, Lee et al., arXiv 2024. [Paper]
    Gecko: 从大型语言模型中提取的多功能文本嵌入
13. Improving Text Embeddings with Large Language Models, Wang et al., ACL 2024. [Paper]
    使用大型语言模型改进文本嵌入

2.2利用LLMs增强模型架构

1. Text and Code Embeddings by Contrastive Pre-Training, Neelakantan et al., arXiv 2022. [Paper]
   通过对比预训练生成文本和代码嵌入
2. Fine-Tuning LLaMA for Multi-Stage Text Retrieval, Ma et al., arXiv 2023. [Paper]
   微调LLaMA进行多阶段文本检索
3. Large Dual Encoders Are Generalizable Retrievers, Ni et al., EMNLP 2022. [Paper]
   大型双编码器是可泛化的检索器
4. Task-aware Retrieval with Instructions, Asai et al., ACL 2023 (Findings). [Paper]
   任务感知检索与指令
5. Transformer memory as a differentiable search index, Tay et al., NeurIPS 2022. [Paper]
   Transformer记忆作为可微分搜索索引
6. Large Language Models are Built-in Autoregressive Search Engines, Ziems et al., ACL 2023 (Findings). [Paper]
   大型语言模型是内置的自回归搜索引擎
7. Chatretriever: Adapting large language models for generalized and robust conversational dense retrieval, Mao et al., arXiv. [Paper]
   Chatretriever: 适应大型语言模型进行通用且鲁棒的对话密集检索
8. How does generative retrieval scale to millions of passages?, Pradeep et al., ACL 2023. [Paper]
   生成式检索如何扩展到数百万段落?, Pradeep et al
9. CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks, Li et al., SIGIR. [Paper]
   CorpusLM: 面向知识密集型任务的统一语言模型

3.重排器

3.1利用LLMs作为监督重排器

1. Multi-Stage Document Ranking with BERT, Nogueira et al., arXiv 2019. [Paper]
   使用BERT进行多阶段文档排序
2. Document Ranking with a Pretrained Sequence-to-Sequence Model, Nogueira et al., EMNLP 2020 (Findings). [Paper]
   使用预训练序列到序列模型进行文档排序
3. Text-to-Text Multi-view Learning for Passage Re-ranking, Ju et al., SIGIR 2021 (Short Paper). [Paper]
   用于段落重排的多视图学习文本到文本模型
4. The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models, Pradeep et al., arXiv 2021. [Paper]
   Expando-Mono-Duo设计模式: 使用预训练序列到序列模型进行文本排序
5. RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses, Zhuang et al., SIGIR 2023 (Short Paper). [Paper]
   RankT5: 使用排序损失微调T5进行文本排序
6. Fine-Tuning LLaMA for Multi-Stage Text Retrieval, Ma et al., arXiv 2023. [Paper]
   微调LLaMA进行多阶段文本检索
7. A Two-Stage Adaptation of Large Language Models for Text Ranking, Zhang et al., ACL 2024 (Findings). [Paper]
   大型语言模型在文本排序中的两阶段适应
8. Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models, Zhang et al., arXiv 2023. [Paper]
   Rank-without-GPT: 基于开源大型语言模型构建独立于GPT的列表式重排器
9. ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval, Yoon et al., ACL 2024. [Paper]
   ListT5: 使用融合解码器进行列表式重排以改进零样本检索
10. Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models, Peng et al., arXiv 2024. [Paper]
    Q-PEFT: 基于查询的参数高效微调用于大型语言模型的文本重排
11. Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models, Liu et al., arXiv 2024. [Paper]
    利用段落嵌入进行高效列表式重排与大型语言模型

3.2利用LLMs作为无监督重排器

1. Holistic Evaluation of Language Models, Liang et al., arXiv 2022. [Paper]
   语言模型的整体评估
2. Improving Passage Retrieval with Zero-Shot Question Generation, Sachan et al., EMNLP 2022. [Paper]
   通过零样本问题生成改进段落检索
3. Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker, Cho et al., ACL 2023 (Findings). [Paper]
   通过约束生成进行离散提示优化以实现零样本重排
4. Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking, Zhuang et al., EMNLP 2023 (Findings). [Paper]
   开源大型语言模型是文档排序的强零样本查询似然模型
5. PaRaDe: Passage Ranking using Demonstrations with Large Language Models, Drozdov et al., EMNLP 2023 (Findings). [Paper]
   PaRaDe: 使用大型语言模型进行段落排序的演示
6. Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels, Zhuang et al., arXiv 2023. [Paper]
   超越是与否: 通过细粒度相关性标签评分改进零样本LLM排序器
7. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent, Sun et al., EMNLP 2023. [Paper]
   ChatGPT在搜索中表现如何? 探究大型语言模型作为重排代理
8. Zero-Shot Listwise Document Reranking with a Large Language Model, Ma et al., arXiv 2023. [Paper]
   零样本列表式文档重排与大型语言模型
9. Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models, Tang et al., arXiv 2023. [Paper]
   中间发现: 排列自一致性改进大型语言模型中的列表式排序
10. Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting, Qin et al., NAACL 2024 (Findings). [Paper]
    大型语言模型通过成对排序提示进行有效文本排序
11. A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models, Zhuang et al., SIGIR 2024. [Paper]
    一种集合方法用于有效且高效的零样本排序与大型语言模型
12. InstUPR: Instruction-based Unsupervised Passage Reranking with Large Language Models, Huang and Chen, arXiv 2024. [Paper]
    InstUPR: 基于指令的无监督段落重排与大型语言模型
13. Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers, Guo et al., arXiv 2024. [Paper]
    即时生成多样化标准以改进点式LLM排序器
14. DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task, Liu et al., arXiv 2024. [Paper]
    DemoRank: 在排序任务中为大型语言模型选择有效演示
15. An Investigation of Prompt Variations for Zero-shot LLM-based Rankers, Sun et al., arXiv 2024. [Paper]
    零样本LLM排序器的提示变体研究
16. TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy, Chen et al., arXiv 2024. [Paper]
    TourRank: 利用大型语言模型通过锦标赛启发策略进行文档排序
17. Top-Down Partitioning for Efficient List-Wise Ranking, Parry et al., arXiv 2024. [Paper]
    自顶向下分区以实现高效列表式排序
18. PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking, Luo et al., ACL 2024. [Paper]
    PRP-Graph: 通过图聚合进行成对排序提示以实现有效文本重排
19. Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing, Yan et al., arXiv 2024. [Paper]
    通过后处理整合大型语言模型的排序和相关性预测

3.3利用LLMs进行训练数据增强

1. ExaRanker: Explanation-Augmented Neural Ranker, Ferraretto et al., SIGIR 2023 (Short Paper). [Paper]
   ExaRanker: 解释增强型神经排序器
2. InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers, Boytsov et al., arXiv 2023. [Paper]
   InPars-Light: 高效排序器的成本效益无监督训练
3. Generating Synthetic Documents for Cross-Encoder Re-Rankers, Askari et al., arXiv 2023. [Paper]
   生成合成文档用于跨编码器重排序器
4. Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers, Sun et al., arXiv 2023. [Paper]
   指令蒸馏使大型语言模型成为高效的零样本排序器
5. RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models, Pradeep et al., arXiv 2023. [Paper]
   RankVicuna: 使用开源大型语言模型进行零样本列表式文档重排序
6. RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!, Pradeep et al., arXiv 2023. [Paper]
   RankZephyr: 有效且稳健的零样本列表式重排序轻而易举！
7. ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs, Ferraretto et al., arXiv 2024. [Paper]
   ExaRanker-Open: 使用开源 LLM 为信息检索生成合成解释
8. Expand, Highlight, Generate: RL-driven Document Generation for Passage Reranking, Askari et al., EMNLP 2023. [Paper]
   扩展、突出、生成：RL 驱动的文档生成用于段落重排序
9. FIRST: Faster Improved Listwise Reranking with Single Token Decoding, Reddy et al., arXiv 2024. [Paper]
   FIRST: 使用单令牌解码的更快改进列表式重排序

4.阅读器

4.1被动阅读器

1. REALM: Retrieval-Augmented Language Model Pre-Training, Guu et al., ICML 2020. [Paper]
   REALM: 检索增强型语言模型预训练
2. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Lewis et al., NeurIPS 2020. [Paper]
   知识密集型 NLP 任务的检索增强生成
3. REPLUG: Retrieval-Augmented Black-Box Language Models, Shi et al., arXiv 2023. [Paper]
   REPLUG: 检索增强型黑箱语言模型
4. Atlas: Few-shot Learning with Retrieval Augmented Language Models, Izacard et al., JMLR 2023. [Paper]
   Atlas: 使用检索增强型语言模型进行少样本学习
5. Internet-augmented Language Models through Few-shot Prompting for Open-domain Question Answering, Lazaridou et al., arXiv 2022. [Paper]
   通过少样本提示增强互联网的语言模型用于开放域问答
6. Rethinking with Retrieval: Faithful Large Language Model Inference, He et al., arXiv 2023. [Paper]
   通过检索重新思考：忠实的大型语言模型推理
7. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation, Vu et al., arxiv 2023. [Paper]
   FreshLLMs: 通过搜索引擎增强刷新大型语言模型
8. Enabling Large Language Models to Generate Text with Citations, Gao et al., EMNLP 2023. [Paper]
   使大型语言模型生成带有引用的文本
9. Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models, Yu et al., arxiv 2023. [Paper]
   Chain-of-Note: 增强检索增强型语言模型的鲁棒性
10. Improving Retrieval-Augmented Large Language Models via Data Importance Learning, Lyu et al., arXiv 2023. [Paper]
    通过数据重要性学习改进检索增强型大型语言模型
11. Search Augmented Instruction Learning, Luo et al., EMNLP 2023 (Findings). [Paper]
    搜索增强指令学习
12. RADIT: Retrieval-Augmented Dual Instruction Tuning, Lin et al., arXiv 2023. [Paper]
    RADIT: 检索增强型双重指令微调
13. Improving Language Models by Retrieving from Trillions of Tokens, Borgeaud et al., ICML 2022. [Paper]
    通过从万亿个标记中检索改进语言模型
14. In-Context Retrieval-Augmented Language Models, Ram et al., arXiv 2023. [Paper]
    上下文检索增强型语言模型
15. Interleaving Retrieval with Chain-of-thought Reasoning for Knowledge-intensive Multi-step Questions, Trivedi et al., ACL 2023. [Paper]
    通过链式思维推理与检索交织解决知识密集型多步骤问题
16. Improving Language Models via Plug-and-Play Retrieval Feedback, Yu et al., arXiv 2023. [Paper]
    通过即插即用检索反馈改进语言模型
17. Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, Shao et al., EMNLP 2023 (Findings). [Paper]
    通过迭代检索-生成协同增强检索增强型大型语言模型
18. Retrieval-Generation Synergy Augmented Large Language Models, Feng et al., arXiv 2023. [Paper]
    检索-生成协同增强大型语言模型
19. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, Asai et al., arXiv 2023. [Paper]
    Self-RAG: 通过自我反思学习检索、生成和批判
20. Active Retrieval Augmented Generation, Jiang et al., EMNLP 2023. [Paper]
    主动检索增强生成

4.2主动阅读器

1. Measuring and Narrowing the Compositionality Gap in Language Models, Press et al., arXiv 2022. [Paper]
   测量并缩小语言模型中的组合性差距
2. DEMONSTRATE–SEARCH–PREDICT: Composing Retrieval and Language Models for Knowledge-intensive NLP, Khattab et al., arXiv 2022. [Paper]
   DEMONSTRATE–SEARCH–PREDICT: 组合检索和语言模型用于知识密集型 NLP
3. Answering Questions by Meta-Reasoning over Multiple Chains of Thought, Yoran et al., arXiv 2023. [Paper]
   通过元推理多条思维链回答问题
4. PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers, Lee ei al., arXiv 2024. [Paper]
   PlanRAG: 计划-检索增强生成用于生成大型语言模型作为决策者
5. Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs, Wang et al., arXiv 2024. [Paper]
   从知识图谱中学习检索增强型大型语言模型的规划

4.3压缩器

1. LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs, Arefeen et al., arXiv 2023. [Paper]
   LeanContext: 使用 LLM 进行成本效益的特定领域问答
2. RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation, Xu et al., arXiv 2023. [Paper]
   RECOMP: 通过压缩和选择性增强改进检索增强型 LMs
3. TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction, Liu et al., EMNLP 2023 (Findings). [Paper]
   TCRA-LLM: 令牌压缩检索增强型大型语言模型用于推理成本降低
4. Learning to Filter Context for Retrieval-Augmented Generation, Wang et al., arXiv 2023. [Paper]
   学习过滤上下文用于检索增强型生成

4.4分析

1. Lost in the Middle: How Language Models Use Long Contexts, Liu et al., arXiv 2023. [Paper]
   迷失：语言模型如何使用长上下文
2. Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation, Ren et al., arXiv 2023. [Paper]
   通过检索增强调查大型语言模型的事实知识边界
3. Exploring the Integration Strategies of Retriever and Large Language Models, Liu et al., arXiv 2023. [Paper]
   探索检索器和大型语言模型的集成策略
4. Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models, Aksitov et al., arXiv 2023. [Paper]
   检索增强型大型语言模型的归因和流畅性权衡特征
5. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories, Mallen et al., ACL 2023. [Paper]
   何时不信任语言模型：调查参数和非参数记忆的有效性

4.5应用

1. Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering, Wang et al., arXiv 2023. [Paper]
   通过医学教科书增强黑箱 LLM 用于临床问答
2. ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science, Munikoti et al., arXiv 2023. [Paper]
   ATLANTIC: 结构感知检索增强型语言模型用于跨学科科学
3. Crosslingual Retrieval Augmented In-context Learning for Bangla, Li et al., arXiv 2023. [Paper]
   跨语言检索增强型上下文学习用于孟加拉语
4. Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature, Lozano et al., arXiv 2023. [Paper]
   Clinfo.ai: 一个开源检索增强型大型语言模型系统，使用科学文献回答医学问题
5. Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models, Zhang et al., ICAIF 2023. [Paper]
   通过检索增强型大型语言模型增强金融情感分析
6. Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models, Louis et al., arXiv 2023. [Paper]
   通过检索增强型大型语言模型进行可解释的长篇法律问答
7. RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit, Liu et al., arXiv 2023. [Paper]
   RETA-LLM: 一个检索增强型大型语言模型工具包
8. Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models, Jiang et al., arXiv 2023. [Paper]
   Chameleon: 一个用于检索增强型语言模型的异构和解耦加速器系统
9. RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models, Hoshi et al., EMNLP 2023. [Paper]
   RaLLe: 一个开发和评估检索增强型大型语言模型的框架
10. Don’t forget private retrieval: distributed private similarity search for large language models, Zyskind et al., arXiv 2023. [Paper]
    不要忘记私有检索：分布式私有相似性搜索用于大型语言模型

5.搜索代理

5.1静态代理

1. LaMDA: Language Models for Dialog Applications, Thoppilan et al., arXiv 2022. [Paper]
   LaMDA: 对话应用的语言模型
2. Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion, Shuster et al., EMNLP 2022 (Findings). [Paper]
   寻求知识的语言模型：对话和提示完成的模块化搜索与生成
3. Teaching language models to support answers with verified quotes, Menick et al., arXiv 2022. [Paper]
   教授语言模型用已验证的引文支持答案
4. WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences, Liu et al., KDD 2023. [Paper]
   WebGLM: 基于人类偏好的高效网页增强问答系统
5. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, Gur et al., arXiv 2023. [Paper]
   一个具有规划、长上下文理解和程序合成的真实世界WebAgent
6. Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher, Shi et al., arXiv 2023. [Paper]
   让LLM成为相关、负责且值得信赖的搜索者
7. CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models, Gong et al., SIGIR 2024. [Paper]
   CoSearchAgent: 基于大型语言模型的轻量级协作搜索代理
8. TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision, Zhou et al., SIGIR 2024. [Paper]
   TRAD: 通过逐步思维检索和对齐决策增强LLM代理

5.2动态代理

1. WebGPT: Browser-assisted question-answering with human feedback, Nakano et al., arXiv 2021. [Paper]
   WebGPT: 基于人类反馈的浏览器辅助问答
2. WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents, Yao et al., arXiv 2022. [Paper]
   WebShop: 基于接地语言代理的可扩展真实世界网页交互
3. WebCPM: Interactive Web Search for Chinese Long-form Question Answering, Qin et al., ACL 2023. [Paper]
   WebCPM: 中文长篇问答的互动网页搜索
4. Mind2Web: Towards a Generalist Agent for the Web, Deng et al., arXiv 2023. [Paper]
   Mind2Web: 面向网页的通用代理
5. WebArena: A Realistic Web Environment for Building Autonomous Agents, Zhou et al., arXiv 2023. [Paper]
   WebArena: 构建自主代理的真实网页环境
6. Hierarchical Prompting Assists Large Language Model on Web Navigation, Sridhar et al., EMNLP 2023 (Findings). [Paper]
   分层提示辅助大型语言模型进行网页导航
7. KwaiAgents: Generalized Information-seeking Agent System with Large Language Models, Pan et al., arXiv 2023. [Paper]
   KwaiAgents: 基于大型语言模型的广义信息寻求代理系统
8. WebVoyager : Building an End-to-End Web Agent with Large Multimodal Models, He et al., arXiv 2024. [Paper]
   WebVoyager : 基于大型多模态模型的端到端网页代理
9. AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent, Lai et al., KDD 2024. [Paper]
   AutoWebGLM: 基于大型语言模型的网页导航代理的引导与强化
10. WebCanvas: Benchmarking Web Agents in Online Environments, Pan et al., arXiv 2024. [Paper]
    WebCanvas: 在线环境中网页代理的基准测试
11. Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence, Chen et al., arXiv 2024. [Paper]
    Internet of Agents: 编织异构代理网络以实现协作智能
12. Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems, Abuelsaad et al., arXiv 2024. [Paper]
    Agent-E: 从自主网页导航到代理系统基础设计原则
13. MindSearch: Mimicking Human Minds Elicits Deep AI Searcher, Chen et al., arXiv 2024. [Paper]
    MindSearch: 模仿人类思维激发深度AI搜索者

6.其他资源

1. ACL 2023 Tutorial: Retrieval-based Language Models and Applications, Asai et al., ACL 2023. [Link]
   ACL 2023 教程: 基于检索的语言模型及其应用
2. A Survey of Large Language Models, Zhao et al., arXiv 2023. [Paper]
   大型语言模型综述
3. Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community, Ai et al., arXiv 2023. [Paper]
   信息检索与大型语言模型相遇: 中国IR社区的战略报告