Information Retrieval

Also known as IR

Information retrieval develops methods for finding useful documents, passages, products, tables, code, and multimedia in response to a user's query. Systems range from lexical methods such as BM25 and learned sparse retrieval to dense embeddings, late-interaction models, hybrid ranking, and graph-based or agentic search. These methods support applications including web search, recommendation, and retrieval-augmented generation, where retrieved evidence must be both relevant and sufficient for the task. A central challenge is balancing accuracy with speed and cost: semantic similarity can miss structured, compositional, or domain-specific evidence, and strong retrieval scores do not always lead to useful answers.

Latest papers

476 papers61 papers in the last four weeks, up 36% on the four weeks before. 0.4% of all new papers.

DataDignity: Training Data Attribution for Large Language Models
May 7, 2026Xiaomin Li, Andrzej Banburski-Fahey, Jaron LanierLLM Auditing Information Retrieval
Microsoft
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
May 7, 2026Susheel Suresh, Hazel Mak, Shangpo Chou +2Tool-Augmented Language Model Agents Agentic Search
Microsoft Corporation
Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
May 6, 2026Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2Remote Sensing Image Understanding Image-Text Retrieval
NEC Laboratories America Princeton, NJ, USA
Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval
May 6, 2026Nicholas Barnfield, Juno Kim, Eshaan Nichani +2Information Retrieval Associative Memory
Harvard University · University of California, Berkeley · Princeton University
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
May 6, 2026Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5Tabular Foundation Models Tabular ML
Natural Language Processing Lab, Soochow University, Suzhou, China · Ant Group, Hangzhou, China · The Hong Kong Polytechnic University, Hong Kong, China
Beyond Retrieval: A Multitask Benchmark and Model for Code Search
May 6, 2026Siqiao Xue, Zihan Liao, Jin Qin +4Learning to Rank Software Engineering Benchmarks
Ant Group · Alipay
SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States
May 6, 2026Zhenliang Zhang, Wenqing Wang, Yong Hu +4Document Understanding Long-Context QA
Peking University · WeChat, Tencent · Work done during an internship at WeChat, Tencent
Reproducing Complex Set-Compositional Information Retrieval
May 5, 2026Vincent Degenhart, Dewi Timman, Arjen P. de Vries +2Query Complexity Lexical Retrieval
Radboud University · Nijmegen, NL
The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles
May 2, 2026Hiun Kim, Tae Kwan Lee, Taeryun WonLanguage Model Pretraining Masked Language Modeling
Naver Seongnam, South Korea
Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
May 2, 2026Jing Dong, Prakirt Raj Jhunjhunwala, Yash KanoriaInformation Retrieval Recommender Systems
Columbia Business School, Columbia University · Amazon.com Inc.
Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?
May 1, 2026Lennard C. Froma, Tom Kouwenhoven, Maaike H. T. de Boer +2LLM Evaluation Language Model Scaling
Leiden University - The Netherlands · TNO - The Netherlands · TU Delft - The Netherlands
H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations
May 1, 2026Passant Elchafei, Hossam Emam, Mohamed Alansary +2Retrieval-Augmented Generation Information Retrieval
1Johannes Kepler University Linz, Institute of Computational Perception, Linz, Austria · 3Independent Researcher, Egypt · 2Linz Institute of Technology, Artificial Intelligence Lab, Austria
FollowTable: A Benchmark for Instruction-Following Table Retrieval
May 1, 2026Rihui Jin, Yuchen Lu, Ting Zhang +7Information Retrieval Table Retrieval
Southeast University Nanjing, China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education · Huawei Noah’s Ark Lab Shenzhen, China
EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory
Apr 30, 2026Yuyang Li, Yime He, Zeyu Zhang +1Temporal IR Evidence Retrieval
The Australian National University · UNSW Sydney
From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
Apr 30, 2026Yilun Zhu, Nikhita Vedula, Shervin MalmasiKG Construction E-Commerce Search
Amazon.com, Inc. Seattle, USA
Hypencoder Revisited: Reproducibility and Analysis of Non-Linear Scoring for First-Stage Retrieval
Apr 29, 2026Arne Eichholtz, Yongkang Li, Jutte Vijverberg +2Learning to Rank Hypernetworks
University of Amsterdam Amsterdam, The Netherlands
Health System Scale Semantic Search Across Unstructured Clinical Notes
Apr 28, 2026Faith Wavinya Mutinda, Spandana Makeneni, Anna Lin +14Healthcare Semantic Search
1) Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA · 2) Google Cloud, Sunnyvale, CA · 3) Department of Pediatrics, University of Pennsylvania, Philadelphia, PA +2
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval
Apr 28, 2026Guosheng Zhang, Linkai Liu, Keyao Wang +3Multimodal Grounding Cross-Modal Alignment
Baidu Inc.
UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
Apr 28, 2026Jongyoon Kim, Minseong Hwang, Seung-won HwangUnsupervised Domain Adaptation Domain Adaptation
Interdisciplinary Program in Artificial Intelligence, Seoul National University
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering
Apr 27, 2026Yuqing Fu, Yimin Deng, Wanyu Wang +9Multi-Hop QA Question Decomposition
City University of Hong Kong · Xi’an Jiaotong University · Michigan State University +2
Adaptive ToR: Complexity-Aware Tree-Based Retrieval for Pareto-Optimal Multi-Intent NLU
Apr 27, 2026Hee-Kyong Yoo, Wonbae Kim, Hyocheol AhnAdaptive Retrieval Intent Classification
Data Science Lab Co., Ltd., Seoul, Korea
Improving Robustness of Tabular Retrieval via Representational Stability
Apr 27, 2026Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao +2Retrieval Robustness Information Retrieval
Department of Computer Science Arizona State University Tempe, Arizona, USA · ScaleAI San Francisco, CA, USA
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
Apr 25, 2026Teng Chen, Sheng Xu, Feixiang Guo +4Language Model Distillation Information Retrieval
Geely AI Lab Ningbo, Zhejiang, China
Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
Apr 23, 2026Seyed Moein Abtahi, Rasa Rahnema, Hetkumar Patel +3Agent Memory Information Retrieval
Moorcheh AI EdgeAI Innovations
Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask
Apr 23, 2026Ashley N. Abraham, Andrew Strelzoff, Haley R. Dozier +2Information Retrieval Product Quantization
Information Technology Laboratory, U.S Army Engineer Research and Development Center, Vicksburg, MS, USA · Environmental Laboratory, U.S Army Engineer Research and Development Center,2026 Vicksburg, MS, USA
From Tokens to Concepts: Leveraging SAE for SPLADE
Apr 23, 2026Yuxuan Zong, Mathias Vast, Basile Van Cooten +2Learned Sparse Retrieval Sparse Autoencoders
Sorbonne Université, CNRS, ISIR · Sinequa by ChapsVision · CNRS, Sorbonne Université, ISIR
IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
Apr 23, 2026Fengbo Ma, Zixin Rao, Xiaoting Li +5Agentic Retrieval LLM Agents
University of Georgia, Athens, GA, USA
Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
Apr 22, 2026Xuhong He, To Eun Kim, Maik Fröbe +3LLM Prompting Multilingual IR
Carnegie Mellon University · Pittsburgh, PA, USA · Friedrich-Schiller-Universität Jena +5
HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
Apr 22, 2026Peng Peng, Weiwei Lin, Wentai Wu +2Retrieval-Augmented Generation Query Re-Identification
South China University of Technology Guangzhou, China · Pengcheng Laboratory Shenzhen, China · Jinan University Guangzhou, China +1
Fast-then-Fine: A Two-Stage Framework with Multi-Granular Representation for Cross-Modal Retrieval in Remote Sensing
Apr 22, 2026Xi Chen, Xu Chen, Xiangyang Jia +3Representation Learning Cross-Modal Alignment
School of Computer Science, Wuhan University, Wuhan, China · Beijing Institute for General Artificial Intelligence (BIGAI), Beijing, China

May 7, 2026J/K move · Enter open · S save

DataDignity: Training Data Attribution for Large Language Models

Xiaomin Li, Andrzej Banburski-Fahey, Jaron Lanier

Microsoft

LLM Auditing Information Retrieval Training Data Attribution Data Provenance in Language Models

Open paper AskPDF arXiv

Auditing language-model outputs often requires more than judging correctness: an auditor may need to identify which source document most likely supports the knowledge expressed in a response. We study this as pinpoint provenance: given a prompt, a target-model response, and a candidate corpus, rank the documents that best support the response. We introduce FakeWiki, a controlled benchmark of 3,537 fabricated Wikipedia-style articles designed to preserve ground-truth provenance while weakening lexical shortcuts. FakeWiki includes QA probes, source-preserving paraphrases, retro-generated variants, hard anti-documents that remain topically similar while removing answer-critical facts, and five query conditions: clean prompting plus four jailbreak-inspired transformations. We evaluate seven retrieval baselines, a training-free activation-steering retrieval-fusion method, SteerFuse, and a supervised contrastive provenance ranker, ScoringModel. ScoringModel maps response and document features into a shared space and is trained with InfoNCE using in-batch, retrieval-mined, and anti-document negatives. Across nine open-weight instruction-tuned LLMs and five query conditions, ScoringModel improves mean Recall@10 from 35.0 for the strongest retrieval baseline to 52.2, without inference-time fusion, and wins 41/45 model-by-condition cells. SteerFuse is usually second-best despite requiring no supervised training, showing that activation-space evidence can efficiently complement text retrieval. On jailbreak-inspired transformed queries, ScoringModel improves Recall@10 by 15.7 points on average over the best baseline. Overall, our work shows that robust training data attribution requires evaluation settings that separate true answer support from topical or lexical resemblance.