DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models
Open accessAbstract
Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs).There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is triggered (determining what to retrieve).However, current dynamic RAG methods fall short in both aspects.Firstly, the strategies for deciding when to retrieve often rely on static rules.Moreover, the strategies for deciding what to retrieve typically limit themselves to the LLM's most recent sentence or the last few tokens, while the LLM's information needs may span across the entire context.To overcome these limitations, we introduce a new framework, DRAGIN, i.e., Dynamic Retrieval Augmented Generation based on the Information Needs of LLMs.Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's information needs during the text generation process.We evaluate DRAGIN along with existing methods comprehensively over 4 knowledge-intensive generation datasets.Experimental results show that DRAGIN achieves superior performance on all tasks, demonstrating the effectiveness of our method 1 .
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Su et al. formulate queries based on the internal attention distribution of LLMs and improve RAG performance by nearly 20% on several benchmark datasets without changing the retrieval system.
“Su et al. [111] directly formulate queries based on the internal attention distribution of LLMs (Figure 2) and improve the performance of RAG for nearly 20% on several benchmark datasets without changing the retrieval system.”
From Foundations of GenIR (2025) · cites this paper as [111]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Still being checked.
Research works printed but not linked: 43
- Sebastian Borgeaud, Arthur Mensch, Jordan Hoff- mann, Trevor Cai, Eliza Rutherford, Katie Milli- can, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from tril- lions of tokens. In International conference on ma- chine learning, pages 2206-2240. PMLR.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
- Jia Chen, Haitao Li, Weihang Su, Qingyao Ai, and Yiqun Liu. 2023. Thuir at wsdm cup 2023 task 1: Unbiased learning to rank. arXiv preprint arXiv:2304.12650. · arXiv
- Xuesong Chen, Ziyi Ye, Xiaohui Xie, Yiqun Liu, Xi- aorong Gao, Weihang Su, Shuqi Zhu, Yike Sun, Min Zhang, and Shaoping Ma. 2022. Web search via an efficient and effective brain-machine interface. In Proceedings of the Fifteenth ACM International Con- ference on Web Search and Data Mining, pages 1569- 1572.
- Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311. · arXiv
- Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, and Yiqun Liu. 2024. Scaling laws for dense retrieval. arXiv preprint arXiv:2403.18684. · arXiv
- James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, and Pradeep Dasigi. 2020. Iirc: A dataset of incomplete information reading compre- hension questions. arXiv preprint arXiv:2011.07127. · arXiv
- Luyu Gao and Jamie Callan. 2021. Condenser: a pre-training architecture for dense retrieval. arXiv preprint arXiv:2104.08253. · arXiv
- Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346- 361.
- Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasu- pat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International confer- ence on machine learning, pages 3929-3938. PMLR.
- Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint arXiv:2011.01060. · arXiv
- Gautier Izacard and Edouard Grave. 2020. Leverag- ing passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282. · arXiv
- Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of halluci- nation in natural language generation. ACM Comput- ing Surveys, 55(12):1-38.
- Zhengbao Jiang, Luyu Gao, Jun Araki, Haibo Ding, Zhiruo Wang, Jamie Callan, and Graham Neubig. 2022. Retrieval as attention: End-to-end learning of retrieval and reading within a single transformer. arXiv preprint arXiv:2212.02027. · arXiv
- Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Ac- tive retrieval augmented generation. arXiv preprint arXiv:2305.06983. · arXiv
- Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019. Generalization through memorization: Nearest neighbor language models. arXiv preprint arXiv:1911.00172. · arXiv
- Haitao Li, Jia Chen, Weihang Su, Qingyao Ai, and Yiqun Liu. 2023a. Towards better web search perfor- mance: Pre-training, fine-tuning and learning to rank. arXiv preprint arXiv:2303.04710. · arXiv
- Haitao Li, Weihang Su, Changyue Wang, Yueyue Wu, Qingyao Ai, and Yiqun Liu. 2023b. Thuir@ col- iee 2023: Incorporating structural knowledge into pre-trained language models for legal case retrieval. arXiv preprint arXiv:2305.06812. · arXiv
- Haitao Li, Changyue Wang, Weihang Su, Yueyue Wu, Qingyao Ai, and Yiqun Liu. 2023c. Thuir@ coliee 2023: More parameters and legal knowledge for legal case entailment. arXiv preprint arXiv:2305.06817. · arXiv
- Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang, Jian-Yun Nie, and Ji-Rong Wen. 2023d. The web can be your oyster for improving large language models. arXiv preprint arXiv:2305.10998. · arXiv
- Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, and Bill Dolan. 2021. A token-level reference-free hallucination detection benchmark for free-form text generation. arXiv preprint arXiv:2104.08704. · arXiv
- Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai, and Yiqun Liu. 2023. Caseencoder: A knowledge- enhanced pre-trained model for legal case encoding. arXiv preprint arXiv:2305.05393. · arXiv
- Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factu- ality in abstractive summarization. arXiv preprint arXiv:2005.00661. · arXiv
- Niklas Muennighoff. 2022. Sgpt: Gpt sentence embeddings for semantic search. arXiv preprint arXiv:2202.08904. · arXiv
- Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. In-context retrieval-augmented lan- guage models. arXiv preprint arXiv:2302.00083. · arXiv
- Stephen Robertson, Hugo Zaragoza, et al. 2009. The probabilistic relevance framework: Bm25 and be- yond. Foundations and Trends® in Information Re- trieval, 3(4):333-389.
- Teven Le Scao, Angela Fan, Christopher Akiki, El- lie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022. Bloom: A 176b- parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100. · arXiv
- Hemlata Shelar, Gagandeep Kaur, Neha Heda, and Poorva Agrawal. 2020. Named entity recognition ap- proaches and their comparison for custom ner model. Science & Technology Libraries, 39(3):324-337.
- Weijia Shi, Sewon Min, Michihiro Yasunaga, Min- joon Seo, Rich James, Mike Lewis, Luke Zettle- moyer, and Wen-tau Yih. 2023. Replug: Retrieval- augmented black-box language models. arXiv preprint arXiv:2301.12652. · arXiv
- Weihang Su, Qingyao Ai, Xiangsheng Li, Jia Chen, Yiqun Liu, Xiaolong Wu, and Shengluan Hou. 2023a. Wikiformer: Pre-training with structured information of wikipedia for ad-hoc retrieval. arXiv preprint arXiv:2312.10661. · arXiv
- Weihang Su, Qingyao Ai, Yueyue Wu, Yixiao Ma, Haitao Li, and Yiqun Liu. 2023b. Caseformer: Pre-training for legal case retrieval. arXiv preprint arXiv:2311.00333. · arXiv
- Weihang Su, Xiangsheng Li, Yiqun Liu, Min Zhang, and Shaoping Ma. 2023c. Thuir2 at ntcir-16 session search (ss) task. arXiv preprint arXiv:2307.00250. · arXiv
- Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, and Yiqun Liu. 2024. Unsu- pervised real-time hallucination detection based on the internal states of large language models. arXiv preprint arXiv:2403.06448. · arXiv
- Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023a. Llama: Open and effi- cient foundation language models. arXiv preprint arXiv:2302.13971. · arXiv
- Hugo Touvron, Louis Martin, Kevin Stone, Peter Al- bert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023b. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint arXiv:2307.09288. · arXiv
- Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Interleav- ing retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212.10509. · arXiv
- Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171. · arXiv
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits rea- soning in large language models. Advances in Neural Information Processing Systems, 35:24824-24837.
- Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Ben- gio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answer- ing. arXiv preprint arXiv:1809.09600. · arXiv
- Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, and Min Zhang. 2024. Relevance feedback with brain signals. ACM Transactions on Information Systems, 42(4):1-37.
- ChengXiang Zhai. 2008. Statistical language models for information retrieval. Synthesis lectures on human language technologies, 1(1):1-141.
- Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher De- wan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068. · arXiv
- Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Paco Guzman, Luke Zettlemoyer, and Marjan Ghazvininejad. 2020. Detecting hallucinated con- tent in conditional neural sequence generation. arXiv preprint arXiv:2011.02593. Prompt 4: exemplars of HotpotQA RAG Question: Jeremy Theobald and Christopher Nolan share what profession? Answer: Jeremy Theobald is an actor and producer. Christopher Nolan is a director, producer, and screenwriter. Therefore, they both share the profession of being a producer. So the answer is producer. Question: What film directed by Brian Patrick Butler was inspired by a film directed by F.W. Murnau? Answer: Brian Patrick Butler directed the film The Phantom Hour. The Phantom Hour was inspired by the films such as Nosferatu and The Cabinet of Dr. Caligari. Of these Nosferatu was directed by F.W. Murnau. So the answer is The Phantom Hour. Question: How many episodes were in the South Korean television series in which Ryu Hye-young played Bo-ra? Answer: The South Korean television series in which Ryu Hye-young played Bo-ra is Reply 1988. The number of episodes Reply 1988 has is 20. So the answer is 20. Question: Were Lonny and Allure both founded in the 1990s? Answer: Lonny (magazine) was founded in 2009. Allure (magazine) was founded in 1991. Thus, of the two, only Allure was founded in 1990s. So the answer is no. Question: Vertical Limit stars which actor who also played astronaut Alan Shepard in "The Right Stuff"? Answer: The actor who played astronaut Alan Shepard in "The Right Stuff" is Scott Glenn. The movie Vertical Limit also starred Scott Glenn. So the answer is Scott Glenn. Question: What was the 2014 population of the city where Lake Wales Medical Center is located? Answer: Lake Wales Medical Center is located in the city of Polk County, Florida. The population of Polk County in 2014 was 15,140. So the answer is 15,140. Question: Who was born first? Jan de Bont or Raoul Walsh? Answer: Jan de Bont was born on 22 October 1943. Raoul Walsh was born on March 11, 1887. Thus, Raoul Walsh was born the first. So the answer is Raoul Walsh. Question: In what country was Lost Gravity manufactured? Answer: The Lost Gravity (roller coaster) was manufactured by Mack Rides. Mack Rides is a German company. So the answer is Germany. Context: · arXiv
Cited by
Sorted by citations
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Ling, C., Zhao, X.-J., Lu, J., Deng, C.-Y., Zheng, C., Wang, J., Chowdhury, T., Li, Y.-Q., Cui, H., Zhang, X.-C., et al. (2023) · ACM Computing Surveys · doi:10.1145/3764579 · Cited by 321 · see citing works
Self-citationZhao, X., Liu, S., Yang, S.-Y., & Miao, C. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714782 · Cited by 116 · see citing works
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Su, W., Wang, C., Ai, Q., Hu, Y., Wu, Z., Zhou, Y., & Liu, Y. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.854 · Cited by 81 · see citing works
Self-citationHM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
Liu, P., Liu, X., Yao, R., Liu, J., Meng, S., Wang, D., & Ma, J. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754761 · Cited by 43 · see citing works
Mitigating Entity-Level Hallucination in Large Language Models
Su, W., Tang, Y., Ai, Q., Wang, C., Wu, Z., & Liu, Y. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698403 · Cited by 39 · see citing works
Self-citationXu, K., Zhang, K., Li, J., Huang, W. D., & Wang, Y. (2024) · Electronics · doi:10.3390/electronics14010047 · Cited by 33 · see citing works
Ke, W., Zheng, Y., Li, Y., Xu, H., Nie, D., Wang, P., & He, Y. (2025) · ACM Transactions on Information Systems · doi:10.1145/3768156 · Cited by 30 · see citing works
Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions
Su, W., Ai, Q., Wu, Y., Xie, A., Wang, C., Ma, Y., Li, H., Wu, Z., Liu, Y., & Zhang, M. (2025) · ACM Transactions on Information Systems · doi:10.1145/3735127 · Cited by 23 · see citing works
Self-citationShusterman, R., Waters, A. C., O’Neill, S. K., Bangs, M., Luu, P., & Tucker, D. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01516-2 · Cited by 22 · see citing works
Robust Fine-tuning for Retrieval Augmented Generation against Retrieval Defects
Tu, Y. L., Su, W., Zhou, Y., Liu, Y., & Ai, Q. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730078 · Cited by 19 · see citing works
Self-citationSTARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals
Su, W., Hu, Y., Xie, A., Ai, Q., Bing, Q., Zheng, N., Liu, Y., Shen, W., & Liu, Y. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.625 · Cited by 17 · see citing works
Self-citationContext-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models
He, J., Liu, G., Zhu, B., Zhang, H., Zheng, H., & Wang, X. (2025) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce65916.2025.11065272 · Cited by 17 · see citing works
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
Su, W., Yue, B., Ai, Q., Hu, Y., Li, J., Wang, C., Zhang, K., Wu, Y., & Liu, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730295 · Cited by 16 · see citing works
Self-citationDeep Research: A Systematic SurveyPreprint
Shi, Z., Chen, Y., Li, H., Sun, W., Ni, S., Lyu, Y., Fan, R.-Z., Jin, B., Weng, Y., Zhu, M., et al. (2025) · Preprints.org · doi:10.20944/preprints202511.2077.v1 · Cited by 16 · see citing works
Self-citationKnowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing
Yang, D., Zeng, L., Rao, J., & Zhang, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730018 · Cited by 13 · see citing works
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Yu, S., Cheng, M., Liu, Q., Wang, D., Yang, J., Ouyang, J., Luo, Y., Lei, C., & Chen, E. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761340 · Cited by 12 · see citing works
A Multi-Source Retrieval Question Answering Framework Based on RAG
Wu, R., Chen, S., Su, X., Zhu, Y., Liao, Y., & Wu, J. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667535 · Cited by 11 · see citing works
Dynamic and Parametric Retrieval-Augmented Generation
Su, W., Ai, Q., Zhan, J., Dong, Q., & Liu, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731692 · Cited by 10 · see citing works
Self-citationConversational Search: From Fundamentals to Frontiers in the LLM Era
Mo, F., Meng, C., Aliannejadi, M., & Nie, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731686 · Cited by 8 · see citing works
Adaptive Orchestration of Modular Generative Information Access Systems
Hoveyda, M., Oosterhuis, H., de Vries, A. D., de Rijke, M., & Hasibi, F. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730351 · Cited by 11 · see citing works
A Survey on Retrieval-Augmented Generation Technology in Large Language Models
Guo, J., Yang, H., & Liu, Q. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-4788-3_23 · Cited by 7 · see citing works
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning
Li, B., Wang, M., Fang, G., Zhang, S., & Ye, W. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.196 · Cited by 7 · see citing works
Xu, Y., Zhang, R., Jiang, X., Feng, Y., Xiao, Y., Ma, X., Zhu, R., Chu, X., Zhao, J., & Wang, Y. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.571 · Cited by 7 · see citing works
Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
Macdonald, C., Fang, J., Parry, A., & Meng, Z. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730150 · Cited by 6 · see citing works
Analysis of Large Language Models for Company Annual Reports Based on Retrieval-Augmented Generation
Mokashi, A., Puthuparambil, B., Daniel, C., & Hanne, T. (2025) · Information · doi:10.3390/info16090786 · Cited by 6 · see citing works
Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
Shi, Y., Sun, M., Liu, Z., Yang, M., Fang, Y., Sun, T., & Gu, X. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774905.3794658 · Cited by 6 · see citing works
Long Context vs. RAG: Strategies for Processing Long Documents in LLMs
Li, X., Bai, Y., Jin, B., Zhu, F., Pan, L., & Cao, Y. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731690 · Cited by 6 · see citing works
Zhu, J., Guo, H., Shi, W., Chen, Z., & Meo, P. D. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i24.34809 · Cited by 5 · see citing works
AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting
Schwarz, K., Bollens, K., Aranda, D. A., & Hartmann, M. (2024) · Applied Sciences · doi:10.3390/app142311165 · Cited by 5 · see citing works
SeerahGPT: Retrieval Augmented Generation based Large Language Model
Obaid, S., & Bawany, N. Z. (2024) · International Conference on Open Source Systems and Technologies (ICOSST) · doi:10.1109/icosst64562.2024.10871159 · Cited by 5 · see citing works
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning
Xin, A., Liu, J., Yao, Z., Lee, Z., Cao, S., Hou, L., & Li, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736849 · Cited by 4 · see citing works
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection
Ding, H., Fang, Y., Zhu, R., Jiang, X., Zhang, J., Xu, Y., Liao, W., Chu, X., Zhao, J., & Wang, Y. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.983 · Cited by 4 · see citing works
He, J., Jia, M., Jia, Z., Duan, J., Song, Y., & Wang, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357045 · Cited by 4 · see citing works
PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
Jiao, S., Xiao, X., Wei, Y., Qi, S., Huang, C., Sheng, Q. Z., & Yao, L. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792103 · Cited by 4 · see citing works
Uncertainty Quantification for Retrieval-Augmented Reasoning
Soudani, H., Zamani, H., & Hasibi, F. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809717 · Cited by 4 · see citing works
Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation
Han, J., Mao, Z., Liu, Y., Che, Y., Fu, Z., & Wang, Q. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.522 · Cited by 3 · see citing works
Self-citationExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection
Ding, G., Li, Y., Zhou, Z., Lin, Z., Zha, D., & Li, J. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809648 · Cited by 3 · see citing works
CONFRAG: Confidence-Guided Retrieval-Augmented Generation
Huang, Y., Xu, Y. E., Sun, K., Yan, V., Sun, A. Y., Khan, H., Nguyen, J., Chen, J., Kachuee, M., Lin, Z., et al. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-industry.171 · Cited by 3 · see citing works
Self-citation“Reasoning” with Rhetoric: On the Style-Evidence Tradeoff in LLM-Generated Counter-Arguments
Verma, P., Jaidka, K., & Churina, S. (2025) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v19i1.35913 · Cited by 3 · see citing works
Context-driven cold-start Web traffic forecasting
Zhou, X., Wang, W., Buntine, W., & Bergmeir, C. (2025) · World Wide Web · doi:10.1007/s11280-025-01359-7 · Cited by 2 · see citing works
Enhanced Search Engine Using Retrieval-Augmented Generation (RAG)
Konda, N., Reddy, Y. C. A. P., Rohit, T. L., & Pulabaigari, V. (2025) · International Conference on Artificial Intelligence and Emerging Technologies (ICAIET) · doi:10.1109/icaiet65052.2025.11211325 · Cited by 2 · see citing works
Learnable Game-Theoretic Policy Optimization for Data-Centric Self-Explanation Rationalization
Zhao, Y., Wang, Z., Yu, X., Li, X., Liang, J., & Li, R. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3638864 · Cited by 2 · see citing works
FoRKER: Focused reasoner with knowledge editing and self-reflection
Zhang, C., Li, H., Wang, C., Zhou, Y., & Peng, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129771 · Cited by 2 · see citing works
Mutual information-based retrieval-augmented generation for domain question answering
Lu, H., Zheng, D., & Yang, L. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02624-x · Cited by 2 · see citing works
ChipDRAG: Dynamic RAG for Chip Placement
Sun, K., Yang, Q., & Li, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-8411-6_46 · Cited by 2 · see citing works
In Line with Context: Repository-Level Code Generation via Context Inlining
Hu, C., Zeng, W., Shi, Y., Shen, B., & Gu, X. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3797094 · Cited by 2 · see citing works
Beyond Relevance: Utility-Centric Retrieval in the LLM Era
Zhang, H., Tang, M., Bi, K., & Guo, J. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808641 · Cited by 2 · see citing works
Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
Yan, Y., Zhang, P., Liu, Z., Wang, H., Bian, Y., Li, W., & Hao, X. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809693 · Cited by 2 · see citing works
Lee, J. H., Ali, G., & Hwang, J. (2025) · Computer Animation and Virtual Worlds · doi:10.1002/cav.70048 · Cited by 1 · see citing works
Driver-Guide: A Multimodal Large Language Model-Based Agent for Driving Scene Understanding
Ran, Y., Gao, B., & Yu, Q. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179498 · Cited by 1 · see citing works
Ruan, W., Qi, T., He, J. Y. D., Sun, B., & Zheng, G. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228999 · Cited by 1 · see citing works
Barbiero, L., Agostini, F., Baci, E., Frigo, F., Vianello, M., Pozza, D., & Campese, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00071-2_32 · Cited by 1 · see citing works
AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings
Li, H., Song, W., Liu, A., & Qin, P. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3761996 · Cited by 1 · see citing works
Discovery Omnia: Dynamic RAG for Enhanced Patent Analysis and Systematic Innovation
Avogadri, S., Alzetta, G., & Russo, D. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-08847-5_6 · Cited by 1 · see citing works
Advancing Temporal Sensitive Question Answering through Progressive Multi-Step Reflection
Chen, Z., Min, E., Zhao, X. H., Li, Y., Jia, X., Liao, J., Wang, S., Hu, B., & Yin, D. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761292 · Cited by 1 · see citing works
Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline
Ye, Z., Ren, H., Cai, Y., Huang, Q., Qin, J., Zhu, P., & Gong, S. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.223 · Cited by 1 · see citing works
Leveraging Product Catalog Patterns for Multilingual E-commerce Product Attribute Prediction
Zhang, B., Khan, S. A., & Walter, S. D. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-industry.18 · Cited by 1 · see citing works
KE-LPG: Toward Semantic Refinement for Lesson Plan Generation
Guo, H., Zhu, J., Pan, C., Wang, Q., Zhou, C., & Meng, C. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10808-4 · Cited by 1 · see citing works
Dynamic and Parametric Retrieval Augmented Generation
Su, W., Dong, Q., Ai, Q., & Liu, Y. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769672 · Cited by 1 · see citing works
Self-citationApplication and progress of artificial intelligence agents in drug development
Zhao, D., & Hsieh, C. (2026) · Journal of Zhejiang University (Medical Sciences) · doi:10.3724/zdxbyxb-2025-0697 · Cited by 1 · see citing works
Relative Performance Bandits: An Adaptive RAG Framework with Reward-Aware Exploration
Dai, Y., Li, J., & Li, B. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322957 · Cited by 1 · see citing works
Chen, X., He, B., Lin, H., Han, X., Wang, T., Cao, B., Sun, L., & Sun, Y. (2026) · ACM Transactions on Information Systems · doi:10.1145/3788865 · Cited by 1 · see citing works
Liu, Z., Huang, P., Xu, Z., Li, X., Liu, S., Peng, C., Xin, H., Yan, Y., Wang, S., Han, X., et al. (2026) · AI Open · doi:10.1016/j.aiopen.2026.02.002 · Cited by 1 · see citing works
Wulf, J., Meierhofer, J., & Pascher, N. (2026) · Procedia CIRP · doi:10.1016/j.procir.2025.09.030 · Cited by 1 · see citing works
Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering
Takahashi, Y., Takeuchi, S., Xin, K., Pelat, G., Ikai, Y., Saito, J., Vitale, J., Berkovsky, S., & Beheshti, A. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00316 · Cited by 1 · see citing works
REAP: Enhancing RAG with Recursive Evaluation and Adaptive Planning for Multi-Hop Question Answering
Zhu, Y., Zhou, H., Hong, W., Liu, T., & Wang, N. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i41.40830 · Cited by 1 · see citing works
Generalized Pseudo-Relevance Feedback
Tu, Y., Su, W., Zhou, Y., Liu, Y., Lin, F., Liu, Q., & Ai, Q. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792078 · Cited by 1 · see citing works
Self-citationActive Retrieval-Augmented Generation with Conflict-Fused Uncertainty Quantification
Tang, T., Yue, X., Chen, Y., Shi, J., & Ding, S. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792926 · Cited by 1 · see citing works
Lightweight Adapted Retrieval-Augmented Inference Network for Legal Issues
钱泓廷, Lin, J., Tang, J., Zhou, H., Peng, Y., Liu, L., & Liu, X. (2026) · Electronics · doi:10.3390/electronics15142996 · Cited by 1 · see citing works
Tu, Y. L., Miao, S., Su, W., Liu, Y., & Ai, Q. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808536 · Cited by 1 · see citing works
Self-citationReport on the Search Futures Workshop at ECIR 2026
Azzopardi, L. A., Clarke, C. L. A., Hauff, C., Kim, Y., Roegiest, A., Trippas, J. R., Ren, Z., Zerhoudi, S., Ai, Q., Amirshahi, S., et al. (2026) · ACM SIGIR Forum · doi:10.1145/3841298.3841307 · Cited by 1 · see citing works
Self-citationChen, H., Yan, Y., Mei, S., Che, W., Liu, Z., Shi, Q., Li, X., Fan, Y., Huang, P., Xiong, Q., et al. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1049 · Cited by 1 · see citing works
Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
Self-citationReal-time Retrieval Argumentation and Introduction to Hybrid Retrieval TransformersPreprint
Raj, U. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4885982
LeKUBE: A Knowledge Update BEnchmark for Legal Domain
Wang, C., Su, W., Hu, Y., Ai, Q., Wu, Y., Luo, C., Liu, Y., Zhang, M., & Ma, S. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698407
Self-citationFoundations of Generative Information Retrieval
Ai, Q., Zhan, J., & Liu, Y. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_2
Self-citationRetrieval augmented generation system for dynamic document tagging and query-driven retrieval
Kuchibhotla, S., & Kuchibhotla, S. (2025) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2025.16.1.2156
Closing the Empirical Loop: Autonomous AI Agents Conduct End‐to‐end Research With Human Participants
Wehr, G., Rideaux, R., Fox, A. J., Lightfoot, D. R., Tangen, J. M., Mattingley, J. B., & Ehrhardt, S. E. (2026) · Advanced Science · doi:10.1002/advs.76675
Diverse Human Value Alignment for Large Language Models via Ethical Reasoning
Wang, J., Xue, S., Li, J., & Wang, X. (2025) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v8i3.36744
Podstawski, M., & Кamola, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03711-4_12
POLRAG: A RAG-LLM Framework for Policy Question Answering
Lin, H., Deng, P., Zhong, Q., & Zhu, X. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00179
RACG-DRT: Retrieval Augumented Code Generation Based on Dynamic Revision of Thoughts
Shang, J., Zhang, C., & Zhang, Y. (2025) · doi:10.1109/icbdse65491.2025.11220101
Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models
Lin, Q., Yang, Z., Cai, Y., Yu, D., Xu, X., Li, Y., & Zhou, L. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.921
Enhancing Academic Search with Agentic RAG and Knowledge Graphs: A Case Study on EconBiz
Hajra, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10721-3_77
Li, Y., Wu, D., & Boulet, B. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11391199
Knowledge Boundary Discovery for Large Language Models
Wang, Z., & Lu, Z. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i40.40670
Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
Dai, T., Zhang, C., & Zou, J. (2026) · Jisuanji shenghuojia. · doi:10.54097/8jvwpk07
CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering
Yang, H., Yang, Z., Zhang, X., Wei, W., Zhang, Y., & Yang, L. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792512
Zhang, M., Qin, X., Jia, Z., Wang, S., & Yang, W. (2026) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait69154.2026.11477468
CITRAG : Contradiction Identification and Tracing for Retrieval-Augmented Generation
Huang, S., Lu, J., Xu, Y., Zhang, H., Xu, H., Tang, M., Wang, Y., & Gou, G. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462915
SEARAG: Semantic Entropy-Guided Adaptive Retrieval for Multi-Hop Question Answering
Yu, D., Lin, Q., Yang, Z., & Zhou, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460781
QCA-RAG: Efficient Retrieval for LLMs via Query Complexity Awareness
Zhu, Y., Li, L., Liu, J., Chen, H., Chen, Z., & Xi, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11465003
SES-RAG: An Adaptive Framework with ModernBERT Segmentation and Generative-Alignment Expansion
Gao, C. S., He, H., & Zhang, Q. (2026) · Applied Sciences · doi:10.3390/app16104849
Legal-TREND: Uncertainty Trend Modeling with Domain-Aware Retrieval for Legal Question Answering
Shu, S., Zhong, J., Fang, J., & Li, J. (2026) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit70033.2026.11557610
DynaChain: On-Demand Retrieval-Augmented Generation via Dynamic Reasoning Chain Correction
Cui, H., Chen, Y., Kou, X., Ding, Y., & Wang, H. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567339
Dynamic Graph Navigation via Triplet Chains for Structure-Aware Retrieval-Augmented Generation
Zhao, F., Wu, Y., Liu, X., & Zhao, R. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1360
NeuRAG: End-to-End Neural Knowledge Augmentation via Hyper-Neurons
Zheng, L., Liu, X., & Liu, J. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1516
Lin, Y., Li, Z., Xing, Y., He, P., Cui, Y., Li, Y., Ding, B., Zhou, J., & Tang, J. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.715
CH-RAG: Complexity-Guided Hybrid Retrieval-Augmented for Adaptive LLM Generation
Ren, J., Zheng, X., Li, Q., Qin, X., & Li, X. J. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582347
- Authors
- Su, W., Tang, Y., Ai, Q., Wu, Z., & Liu, Y.
- Affiliations
- Tsinghua University (CN)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 12991-13013
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671152
- Cited by
- 81
- References
- 0
- Citations by year
- 2026: 47 · 2025: 27 · 2024: 7
- FWCI
- 23.90
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, information needs, query generation, text generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
