{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:48:52Z","timestamp":1782492532555,"version":"3.54.5"},"reference-count":43,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013084","name":"Program for Innovation Team Building at Institutions of Higher Education in Chongqing","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013084","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ipm.2026.104994","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:43:55Z","timestamp":1782135835000},"page":"104994","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Learning to encode and activate internal knowledge for retrieval and generation"],"prefix":"10.1016","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9044-2595","authenticated-orcid":false,"given":"Yong","family":"Guan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4130-3924","authenticated-orcid":false,"given":"Shaoru","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2\u20133","key":"10.1016\/j.ipm.2026.104994_b1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1561\/2200000080","article-title":"A tutorial on meta-reinforcement learning","volume":"18","author":"Beck","year":"2025","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"10.1016\/j.ipm.2026.104994_b2","series-title":"From local to global: A graph RAG approach to query-focused summarization","author":"Edge","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b3","series-title":"Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"6491","article-title":"A survey on RAG meeting LLMs: Towards retrieval-augmented large language models","author":"Fan","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b4","series-title":"A survey on RAG meeting LLMs: Towards retrieval-augmented large language models","author":"Fan","year":"2024"},{"issue":"3","key":"10.1016\/j.ipm.2026.104994_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104490","article-title":"Price-sensitive feature analysis from online reviews using a tiered information extraction framework for product optimization","volume":"63","author":"Fu","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104994_b6","series-title":"A survey on LLM-as-a-judge","author":"Gu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b7","doi-asserted-by":"crossref","unstructured":"Guan, Y., Liu, D., Ma, J., Peng, H., Wang, X., Hou, L., & Li, R. (2024). Event GDR: Event-Centric Generative Document Retrieval. In Companion proceedings of the ACM web conference 2024 (pp. 975\u2013978). New York, NY, USA: URL: https:\/\/doi.org\/10.1145\/3589335.3651500.","DOI":"10.1145\/3589335.3651500"},{"key":"10.1016\/j.ipm.2026.104994_b8","series-title":"From RAG to memory: Non-parametric continual learning for large language models","author":"Guti\u00e9rrez","year":"2025"},{"issue":"2, Part B","key":"10.1016\/j.ipm.2026.104994_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104471","article-title":"Enhancing large language model for fake news video detection via cross-modal retrieval","volume":"63","author":"Han","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104994_b10","series-title":"Engineering RAG systems for real-world applications: Design, development, and evaluation","author":"Hasan","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b11","series-title":"Proceedings of the 28th international conference on computational linguistics","first-page":"6609","article-title":"Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps","author":"Ho","year":"2020"},{"key":"10.1016\/j.ipm.2026.104994_b12","series-title":"Findings of the association for computational linguistics","first-page":"9025","article-title":"Towards better question generation in QA-based event extraction","author":"Hong","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b13","series-title":"REINFORCE++: An efficient RLHF algorithm with robustness to both prompt and reward models","author":"Hu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b14","series-title":"A survey on retrieval-augmented text generation for large language models","author":"Huang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b15","series-title":"Proceedings of the 2024 conference of the North American chapter of the association for computational linguistics: Human language technologies (volume 1: long papers)","first-page":"7036","article-title":"Adaptive-RAG: Learning to adapt retrieval-augmented large language models through question complexity","author":"Jeong","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b16","series-title":"R3-RAG: Learning step-by-step reasoning and retrieval for LLMs via reinforcement learning","author":"Li","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b17","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing","first-page":"580","article-title":"Mitigating the alignment tax of rlhf","author":"Lin","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b18","series-title":"Reinforcing compositional retrieval: Retrieving step-by-step for composing informative contexts","author":"Long","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b19","series-title":"Alignment and safety in large language models: Safety mechanisms, training paradigms, and emerging challenges","author":"Lu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b20","series-title":"Online merging optimizers for boosting rewards and mitigating tax in alignment","author":"Lu","year":"2024"},{"issue":"3","key":"10.1016\/j.ipm.2026.104994_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104486","article-title":"Assessing the potential of LLMs as crowdworkers for contextual information generation","volume":"63","author":"Mart\u00ednez-Murillo","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104994_b22","series-title":"Proceedings of the 2025 international ACM SIGIR conference on innovative concepts and theories in information retrieval","first-page":"136","article-title":"W-RAG: Weakly supervised dense retrieval in RAG for open-domain question answering","author":"Nian","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b23","series-title":"LegalBench-RAG: A benchmark for retrieval-augmented generation in the legal domain","author":"Pipitone","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b24","series-title":"Proceedings of the 2023 conference on empirical methods in natural language processing","first-page":"1305","article-title":"How does generative retrieval scale to millions of passages?","author":"Pradeep","year":"2023"},{"key":"10.1016\/j.ipm.2026.104994_b25","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing","first-page":"7371","article-title":"ADELIE: Aligning large language models on information extraction","author":"Qi","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b26","series-title":"Using AI for user representation: An analysis of 83 persona prompts","author":"Salminen","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b27","series-title":"Proceedings of the extended abstracts of the CHI conference on human factors in computing systems","article-title":"\u201cWhen AI writes personas\u201d: Analyzing lexical diversity in LLM-generated persona descriptions","author":"Sethi","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b28","series-title":"DeepSeekMath: Pushing the limits of mathematical reasoning in open language models","author":"Shao","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b29","series-title":"R1-searcher++: Incentivizing the dynamic knowledge acquisition of LLMs via reinforcement learning","author":"Song","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b30","series-title":"Proceedings of the conference on neural information processing systems","first-page":"21831","article-title":"Transformer memory as a differentiable search index","volume":"vol. 35","author":"Tay","year":"2022"},{"key":"10.1016\/j.ipm.2026.104994_b31","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1162\/tacl_a_00475","article-title":"Musique: multihop questions via single-hop question composition","volume":"10","author":"Trivedi","year":"2022","journal-title":"Proceedings of the Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.ipm.2026.104994_b32","series-title":"Llama 3 meets MoE: Efficient upcycling","author":"Vavre","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b33","series-title":"Proceedings of the 2025 international conference on artificial intelligence and machine learning applications theme: Healthcare and internet of things","first-page":"1","article-title":"Revolutionizing legal access: An AI-driven RAG chatbot for real-time judicial insights","author":"Vijayakumaran","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b34","series-title":"Proceedings of the 36th conference on neural information processing systems","article-title":"A neural corpus indexer for document retrieval","author":"Wang","year":"2022"},{"key":"10.1016\/j.ipm.2026.104994_b35","series-title":"Findings of the association for computational linguistics: EMNLP 2023","first-page":"9129","article-title":"Domain adaptation for conversational query production with the RAG model feedback","author":"Wang","year":"2023"},{"issue":"2, Part B","key":"10.1016\/j.ipm.2026.104994_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104437","article-title":"Can large language models replace human experts in knowledge construction? A comparative analysis from the perspectives of information quality, information perception, and information load","volume":"63","author":"Wei","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104994_b37","series-title":"Reinforcement learning in vision: A survey","author":"Wu","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b38","series-title":"MMed-RAG: Versatile multimodal RAG system for medical vision language models","author":"Xia","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b39","series-title":"Proceedings of the 2018 conference on empirical methods in natural language processing","first-page":"2369","article-title":"HotpotQA: A dataset for diverse, explainable multi-hop question answering","author":"Yang","year":"2018"},{"key":"10.1016\/j.ipm.2026.104994_b40","series-title":"IM-RAG: Multi-round retrieval-augmented generation through learning inner monologues","author":"Yang","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b41","series-title":"A survey of graph retrieval-augmented generation for customized large language models","author":"Zhang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104994_b42","series-title":"Retrieval augmented generation (RAG) and beyond: A comprehensive survey on how to make your LLMs use external data more wisely","author":"Zhao","year":"2024"},{"key":"10.1016\/j.ipm.2026.104994_b43","series-title":"Bridging the gap between indexing and retrieval for differentiable search index with query generation","author":"Zhuang","year":"2023"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003857?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003857?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:14:12Z","timestamp":1782490452000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326003857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,1]]}},"alternative-id":["S0306457326003857"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104994","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learning to encode and activate internal knowledge for retrieval and generation","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104994","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104994"}}