{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:42:38Z","timestamp":1776976958544,"version":"3.51.4"},"reference-count":68,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001459","name":"National Research Foundation, Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015311","name":"Infocomm Media Development Authority","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015311","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,24]]},"DOI":"10.1109\/icaiic68212.2026.11454197","type":"proceedings-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:50:24Z","timestamp":1774986624000},"page":"687-692","source":"Crossref","is-referenced-by-count":0,"title":["Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey"],"prefix":"10.1109","author":[{"given":"Yuqing","family":"Zhao","sequence":"first","affiliation":[{"name":"Nanyang Technological University,Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyao","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University,Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongsen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Nanyang Technological University,Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwok-Yan","family":"Lam","sequence":"additional","affiliation":[{"name":"Nanyang Technological University,Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1162\/coli.a.16"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3703155"},{"key":"ref3","article-title":"Cognitive mirage: A review of hallucinations in large language models","author":"Ye","year":"2023","journal-title":"arXiv preprint"},{"key":"ref4","article-title":"A comprehensive survey of hallucination mitigation techniques in large language models","volume":"6","author":"Tonmoy","year":"2024","journal-title":"arXiv preprint"},{"key":"ref5","article-title":"A survey of large language models attribution","author":"Li","year":"2023","journal-title":"arXiv preprint"},{"key":"ref6","article-title":"A comprehensive survey of retrieval-augmented generation (rag): Evolution, current landscape and future directions","author":"Gupta","year":"2024","journal-title":"arXiv preprint"},{"key":"ref7","article-title":"Retrieval-augmented generation for large language models: A survey","volume":"2","author":"Gao","year":"2023","journal-title":"arXiv preprint"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.68"},{"key":"ref9","article-title":"Towards reliable and fluent large language models: Incorporating feedback learning loops in qa systems","author":"Lee","year":"2023","journal-title":"arXiv preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.557"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.knowledgenlp-1.4"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.emnlp-main.187"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.499"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3233\/faia250237"},{"key":"ref15","article-title":"Check your facts and try again: Improving large language models with external knowledge and automated feedback","author":"Peng","year":"2023","journal-title":"arXiv preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"ref17","article-title":"Webgpt: Browserassisted question-answering with human feedback","author":"Nakano","year":"2021","journal-title":"arXiv preprint"},{"key":"ref18","article-title":"Webbrain: Learning to generate factually correct articles for queries by grounding on large web corpus","author":"Qian","year":"2023","journal-title":"arXiv preprint"},{"key":"ref19","first-page":"9459","article-title":"Retrievalaugmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.fever-1.10"},{"key":"ref21","first-page":"3929","article-title":"Retrieval augmented language model pre-training","volume-title":"International conference on machine learning. PMLR","author":"Guu","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.879"},{"key":"ref23","article-title":"Replug: Retrieval-augmented black-box language models","author":"Shi","year":"2023","journal-title":"arXiv preprint"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.136"},{"key":"ref25","article-title":"C-rag: Certified generation risks for retrieval-augmented language models","volume-title":"[Online]. Available","author":"Kang","year":"2024"},{"key":"ref26","article-title":"Self-rag: Learning to retrieve, generate, and critique through self-reflection","volume-title":"The Twelfth International Conference on Learning Representations","author":"Asai","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.13"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.326"},{"key":"ref29","article-title":"Recomp: Improving retrieval-augmented lms with compression and selective augmentation","author":"Xu","year":"2023","journal-title":"arXiv preprint"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.63"},{"key":"ref31","article-title":"Lamda: Language models for dialog applications","author":"Thoppilan","year":"2022","journal-title":"arXiv preprint"},{"key":"ref32","article-title":"Improving alignment of dialogue agents via targeted human judgements","author":"Glaese","year":"2022","journal-title":"arXiv preprint"},{"key":"ref33","volume-title":"Teaching language models to support answers with verified quotes,2022","author":"Menick","year":"2022"},{"key":"ref34","article-title":"Think while you write: Hypothesis verification promotes faithful knowledge-to-text generation","author":"Qiu","year":"2023","journal-title":"arXiv preprint"},{"issue":"251","key":"ref35","first-page":"1","article-title":"Atlas: Few-shot learning with retrieval augmented language models","volume":"24","author":"Izacard","year":"2023","journal-title":"Journal of Machine Learning Research"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2574"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.910"},{"key":"ref39","article-title":"Effective large language model adaptation for improved grounding and citation generation","author":"Ye","year":"2023","journal-title":"arXiv preprint"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.759"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10448015"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.620"},{"key":"ref43","article-title":"The knowledge alignment problem: Bridging human and external knowledge for large language models","author":"Zhang","year":"2023","journal-title":"arXiv preprint"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.967"},{"key":"ref45","article-title":"Complex claim verification with evidence retrieved in the wild","author":"Chen","year":"2023","journal-title":"arXiv preprint"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.615"},{"key":"ref47","article-title":"Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp","author":"Khattab","year":"2022","journal-title":"arXiv preprint"},{"key":"ref48","article-title":"Take a step back: Evoking reasoning via abstraction in large language models","author":"Zheng","year":"2023","journal-title":"arXiv preprint"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.469"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00605"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.99"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"key":"ref54","article-title":"Llatrieval: Llm-verified retrieval for verifiable generation","author":"Li","year":"2023","journal-title":"arXiv preprint"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.590"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.758"},{"key":"ref57","article-title":"\u201daccording to\u2026\u201d: Prompting language models improves quoting from pre-training data","author":"Weller","year":"2023","journal-title":"arXiv preprint"},{"key":"ref58","article-title":"Recitation-augmented language models","author":"Sun","year":"2022","journal-title":"arXiv preprint"},{"key":"ref59","article-title":"Augmented large language models with parametric knowledge guiding","author":"Luo","year":"2023","journal-title":"arXiv preprint"},{"key":"ref60","article-title":"How well do llms cite relevant medical references? an evaluation framework and analyses","author":"Wu","year":"2024","journal-title":"arXiv preprint"},{"key":"ref61","article-title":"Evidence-based factual error correction","author":"Thorne","year":"2020","journal-title":"arXiv preprint"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3624918.3625336"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.806"},{"key":"ref64","article-title":"Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation","author":"Li","year":"2024","journal-title":"arXiv preprint"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i24.34743"},{"key":"ref66","article-title":"Chain-of-verification reduces hallucination in large language models","author":"Dhuliawala","year":"2023","journal-title":"arXiv preprint"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645363"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.320"}],"event":{"name":"2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","location":"Tokyo, Japan","start":{"date-parts":[[2026,2,24]]},"end":{"date-parts":[[2026,2,27]]}},"container-title":["2026 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11454127\/11454137\/11454197.pdf?arnumber=11454197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:55:56Z","timestamp":1776974156000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11454197\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/icaiic68212.2026.11454197","relation":{},"subject":[],"published":{"date-parts":[[2026,2,24]]}}}