{"total":207,"rows":[{"id":"2212.10496","title":"Precise Zero-Shot Dense Retrieval without Relevance Labels","created":"2022-12-20","cited_by_count":517,"area":null},{"id":"2407.12883","title":"BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval","created":"2024-07-16","cited_by_count":158,"area":null},{"id":"2303.07678","title":"Query2doc: Query Expansion with Large Language Models","created":"2023-03-14","cited_by_count":149,"area":null},{"id":"2109.10086","title":"SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval","created":"2021-09-21","cited_by_count":127,"area":null},{"id":"2410.02694","title":"HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly","created":"2024-10-03","cited_by_count":125,"area":null},{"id":"2108.08787","title":"Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval","created":"2021-08-19","cited_by_count":119,"area":null},{"id":"2302.07452","title":"How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval","created":"2023-02-15","cited_by_count":118,"area":null},{"id":"2108.13897","title":"mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset","created":"2021-08-31","cited_by_count":116,"area":null},{"id":"2104.07186","title":"COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List","created":"2021-04-15","cited_by_count":113,"area":null},{"id":"2508.21038","title":"On the Theoretical Limitations of Embedding-Based Retrieval","created":"2025-08-28","cited_by_count":104,"area":null},{"id":"2205.04733","title":"From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective","created":"2022-05-10","cited_by_count":98,"area":null},{"id":"2404.14851","title":"From Matching to Generation: A Survey on Generative Information Retrieval","created":"2024-04-23","cited_by_count":89,"area":null},{"id":"2305.02156","title":"Zero-Shot Listwise Document Reranking with a Large Language Model","created":"2023-05-03","cited_by_count":82,"area":null},{"id":"2309.15088","title":"RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models","created":"2023-09-26","cited_by_count":70,"area":null},{"id":"2304.11370","title":"SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval","created":"2023-04-22","cited_by_count":62,"area":null},{"id":"2310.09497","title":"A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models","created":"2023-10-14","cited_by_count":55,"area":null},{"id":"2305.11841","title":"How Does Generative Retrieval Scale to Millions of Passages?","created":"2023-05-19","cited_by_count":48,"area":null},{"id":"2108.13454","title":"Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback","created":"2021-08-30","cited_by_count":47,"area":null},{"id":"2406.11678","title":"TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy","created":"2024-06-17","cited_by_count":47,"area":null},{"id":"2108.11792","title":"A Statutory Article Retrieval Dataset in French","created":"2021-08-26","cited_by_count":40,"area":null},{"id":"2409.11136","title":"Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models","created":"2024-09-17","cited_by_count":40,"area":null},{"id":"2204.13679","title":"Curriculum Learning for Dense Retrieval Distillation","created":"2022-04-28","cited_by_count":38,"area":null},{"id":"2206.08063","title":"Towards Robust Ranker for Text Retrieval","created":"2022-06-16","cited_by_count":37,"area":null},{"id":"2209.06506","title":"Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models","created":"2022-09-14","cited_by_count":37,"area":null},{"id":"2104.13640","title":"Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers","created":"2021-04-28","cited_by_count":35,"area":null},{"id":"2305.11694","title":"QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations","created":"2023-05-19","cited_by_count":31,"area":null},{"id":"2106.12657","title":"Extreme Multi-label Learning for Semantic Matching in Product Search","created":"2021-06-23","cited_by_count":28,"area":null},{"id":"2406.14449","title":"APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking","created":"2024-06-20","cited_by_count":28,"area":null},{"id":"2103.02280","title":"Simplified Data Wrangling with ir_datasets","created":"2021-03-03","cited_by_count":27,"area":null},{"id":"2304.14233","title":"Large Language Models are Strong Zero-Shot Retriever","created":"2023-04-27","cited_by_count":26,"area":null},{"id":"2309.08541","title":"When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets","created":"2023-09-15","cited_by_count":26,"area":null},{"id":"2204.00716","title":"CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos","created":"2022-04-01","cited_by_count":22,"area":null},{"id":"2507.10865","title":"Overview of the TREC 2022 deep learning track","created":"2025-07-10","cited_by_count":22,"area":null},{"id":"2203.13088","title":"Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction","created":"2022-03-24","cited_by_count":21,"area":null},{"id":"2209.13335","title":"PROD: Progressive Distillation for Dense Retrieval","created":"2022-09-27","cited_by_count":21,"area":null},{"id":"2310.07815","title":"Language Models As Semantic Indexers","created":"2023-10-11","cited_by_count":20,"area":null},{"id":"2404.18185","title":"Ranked List Truncation for Large Language Model-based Re-Ranking","created":"2024-04-28","cited_by_count":20,"area":null},{"id":"2406.14848","title":"Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models","created":"2024-06-21","cited_by_count":20,"area":null},{"id":"2409.16022","title":"AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment","created":"2024-09-24","cited_by_count":20,"area":null},{"id":"2105.12936","title":"Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence","created":"2021-05-27","cited_by_count":19,"area":null},{"id":"2205.11194","title":"UnifieR: A Unified Retriever for Large-Scale Retrieval","created":"2022-05-23","cited_by_count":19,"area":null},{"id":"2404.14600","title":"Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding","created":"2024-04-22","cited_by_count":19,"area":null},{"id":"2206.14719","title":"Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision","created":"2022-06-29","cited_by_count":18,"area":null},{"id":"2310.20158","title":"GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval","created":"2023-10-31","cited_by_count":18,"area":null},{"id":"2404.01012","title":"Query Performance Prediction using Relevance Judgments Generated by Large Language Models","created":"2024-04-01","cited_by_count":18,"area":null},{"id":"2507.08191","title":"Overview of the TREC 2021 deep learning track","created":"2025-07-10","cited_by_count":18,"area":null},{"id":"2204.02292","title":"Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval","created":"2022-04-05","cited_by_count":17,"area":null},{"id":"2402.18031","title":"Corpus-Steered Query Expansion with Large Language Models","created":"2024-02-28","cited_by_count":17,"area":null},{"id":"2405.16546","title":"Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration","created":"2024-05-26","cited_by_count":17,"area":null},{"id":"2406.16332","title":"DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task","created":"2024-06-24","cited_by_count":17,"area":null},{"id":"2208.07670","title":"ConTextual Masked Auto-Encoder for Dense Passage Retrieval","created":"2022-08-16","cited_by_count":16,"area":null},{"id":"2406.00231","title":"LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking","created":"2024-05-31","cited_by_count":16,"area":null},{"id":"2409.07691","title":"Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG","created":"2024-09-12","cited_by_count":16,"area":null},{"id":"2208.13661","title":"LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval","created":"2022-08-29","cited_by_count":15,"area":null},{"id":"2211.12850","title":"OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries","created":"2022-10-22","cited_by_count":15,"area":null},{"id":"2304.11943","title":"Constructing Tree-based Index for Efficient and Effective Dense Retrieval","created":"2023-04-24","cited_by_count":15,"area":null},{"id":"2312.10661","title":"Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval","created":"2023-12-17","cited_by_count":15,"area":null},{"id":"2407.06992","title":"Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2407.20750","title":"JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources","created":"2024-07-30","cited_by_count":15,"area":null},{"id":"2503.19092","title":"Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation","created":"2025-03-24","cited_by_count":15,"area":null},{"id":"2504.12408","title":"A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment","created":"2025-04-16","cited_by_count":15,"area":null},{"id":"2105.03599","title":"Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval","created":"2021-05-08","cited_by_count":14,"area":null},{"id":"2301.02998","title":"InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers","created":"2023-01-08","cited_by_count":14,"area":null},{"id":"2312.02969","title":"Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models","created":"2023-12-05","cited_by_count":14,"area":null},{"id":"2504.12558","title":"Benchmarking LLM-based Relevance Judgment Methods","created":"2025-04-17","cited_by_count":14,"area":null},{"id":"2305.02564","title":"RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models","created":"2023-05-04","cited_by_count":13,"area":null},{"id":"2307.00250","title":"THUIR2 at NTCIR-16 Session Search (SS) Task","created":"2023-07-01","cited_by_count":12,"area":null},{"id":"2307.08303","title":"Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models","created":"2023-07-17","cited_by_count":12,"area":null},{"id":"2504.01818","title":"Efficient Constant-Space Multi-Vector Retrieval","created":"2025-04-02","cited_by_count":12,"area":null},{"id":"2504.07439","title":"LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking","created":"2025-04-10","cited_by_count":12,"area":null},{"id":"2305.07402","title":"Synergistic Interplay between Search and Large Language Models for Information Retrieval","created":"2023-05-12","cited_by_count":11,"area":null},{"id":"2504.17884","title":"Unsupervised Corpus Poisoning Attacks in Continuous Space for Dense Retrieval","created":"2025-04-24","cited_by_count":11,"area":null},{"id":"2112.05662","title":"Match Your Words! A Study of Lexical Matching in Neural Information Retrieval","created":"2021-12-10","cited_by_count":10,"area":null},{"id":"2305.09025","title":"Soft Prompt Decoding for Multilingual Dense Retrieval","created":"2023-05-15","cited_by_count":10,"area":null},{"id":"2203.10282","title":"Clickbait Spoiling via Question Answering and Passage Retrieval","created":"2022-03-19","cited_by_count":9,"area":null},{"id":"2204.11458","title":"ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference","created":"2022-04-25","cited_by_count":9,"area":null},{"id":"2212.09598","title":"Query-as-context Pre-training for Dense Passage Retrieval","created":"2022-12-19","cited_by_count":9,"area":null},{"id":"2507.08890","title":"Overview of the TREC 2023 deep learning track","created":"2025-07-10","cited_by_count":9,"area":null},{"id":"2103.03335","title":"A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models","created":"2021-03-04","cited_by_count":8,"area":null},{"id":"2104.02061","title":"Query2Prod2Vec Grounded Word Embeddings for eCommerce","created":"2021-04-02","cited_by_count":8,"area":null},{"id":"2108.01360","title":"Towards a Better Understanding Human Reading Comprehension with Brain Signals","created":"2021-08-03","cited_by_count":8,"area":null},{"id":"2404.14989","title":"A Reproducibility Study of PLAID","created":"2024-04-23","cited_by_count":8,"area":null},{"id":"2505.18512","title":"AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking","created":"2025-05-24","cited_by_count":8,"area":null},{"id":"2201.05409","title":"Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval","created":"2022-01-14","cited_by_count":7,"area":null},{"id":"2212.07841","title":"MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers","created":"2022-12-15","cited_by_count":7,"area":null},{"id":"2304.10195","title":"CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval","created":"2023-04-20","cited_by_count":7,"area":null},{"id":"2307.16779","title":"Lexically-Accelerated Dense Retrieval","created":"2023-07-31","cited_by_count":7,"area":null},{"id":"2308.08285","title":"Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval","created":"2023-08-16","cited_by_count":7,"area":null},{"id":"2404.04487","title":"KazQAD: Kazakh Open-Domain Question Answering Dataset","created":"2024-04-06","cited_by_count":7,"area":null},{"id":"2404.08535","title":"Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking","created":"2024-04-12","cited_by_count":7,"area":null},{"id":"2411.04602","title":"Self-Calibrated Listwise Reranking with Large Language Models","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2501.19264","title":"mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval","created":"2025-01-31","cited_by_count":7,"area":null},{"id":"2502.05364","title":"Hypencoder: Hypernetworks for Information Retrieval","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2602.21456","title":"Revisiting Text Ranking in Deep Research","created":"2026-02-25","cited_by_count":7,"area":null},{"id":"2110.10778","title":"Contrastive Document Representation Learning with Graph Attention Networks","created":"2021-10-20","cited_by_count":6,"area":null},{"id":"2211.10841","title":"SeDR: Segment Representation Learning for Long Documents Dense Retrieval","created":"2022-11-20","cited_by_count":6,"area":null},{"id":"2212.03222","title":"LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval","created":"2022-12-06","cited_by_count":6,"area":null},{"id":"2406.17968","title":"Efficient Document Ranking with Learnable Late Interactions","created":"2024-06-25","cited_by_count":6,"area":null},{"id":"2411.17299","title":"2D Matryoshka Training for Information Retrieval","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2502.04645","title":"Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25","created":"2025-02-07","cited_by_count":6,"area":null}],"next_cursor":"100"}