{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:53:09Z","timestamp":1763196789062,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533480","type":"print"},{"value":"9789819533497","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3349-7_13","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:50:02Z","timestamp":1763196602000},"page":"159-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensuring Context Completeness in\u00a0Retrieval-Augmented Generation for\u00a0Knowledge-Intensive Question-Answering"],"prefix":"10.1007","author":[{"given":"Yifan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jinming","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lingjiao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Long","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"13_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1162\/tacl_a_00605","volume":"11","author":"O Ram","year":"2023","unstructured":"Ram, O., et al.: In-context retrieval-augmented language models. Trans. Assoc. Comput. Linguistics 11, 1316\u20131331 (2023)","journal-title":"Trans. Assoc. Comput. Linguistics"},{"key":"13_CR3","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877\u20131901 (2020)"},{"key":"13_CR4","unstructured":"Kandpal, N., Deng, H., Roberts, A., Wallace, E., Raffel, C.: Large language models struggle to learn long-tail knowledge. In: International Conference on Machine Learning, pp. 15696\u201315707. PMLR (2023)"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Bang, Y., et\u00a0al.: A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. In: Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 675\u2013718 (2023)","DOI":"10.18653\/v1\/2023.ijcnlp-main.45"},{"key":"13_CR6","unstructured":"Rawte, V., Sheth, A., Das, A.: A survey of hallucination in large foundation models. arXiv preprint arXiv:2309.05922 (2023)"},{"key":"13_CR7","unstructured":"Shi, F., et al.: Large language models can be easily distracted by irrelevant context. In: International Conference on Machine Learning, pp. 31210\u201331227. PMLR (2023)"},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/S0079-7421(09)51009-2","volume":"51","author":"DS McNamara","year":"2009","unstructured":"McNamara, D.S., Magliano, J.: Toward a comprehensive model of comprehension. Psychol. Learn. Motiv. 51, 297\u2013384 (2009)","journal-title":"Psychol. Learn. Motiv."},{"key":"13_CR9","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9459\u20139474 (2020)"},{"key":"13_CR10","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2023)"},{"issue":"4","key":"13_CR11","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1002\/RRQ.027","volume":"47","author":"SR Goldman","year":"2012","unstructured":"Goldman, S.R., Braasch, J.L., Wiley, J., Graesser, A.C., Brodowinska, K.: Comprehending and learning from internet sources: processing patterns of better and poorer learners. Read. Res. Q. 47(4), 356\u2013381 (2012)","journal-title":"Read. Res. Q."},{"key":"13_CR12","unstructured":"Wang, Z., Araki, J., Jiang, Z., Parvez, M.R., Neubig, G.: Learning to filter context for retrieval-augmented generation. arXiv preprint arXiv:2311.08377 (2023)"},{"issue":"1","key":"13_CR13","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/10888438.2013.827687","volume":"18","author":"C Perfetti","year":"2014","unstructured":"Perfetti, C., Stafura, J.: Word knowledge in a theory of reading comprehension. Sci. Stud. Read. 18(1), 22\u201337 (2014)","journal-title":"Sci. Stud. Read."},{"key":"13_CR14","unstructured":"Rouet, J.: Relevance processes in multiple document comprehension. Text relevance and learning from text: IAP (2011)"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Jeong, S., Baek, J., Cho, S., Hwang, S.J., Park, J.C.: Adaptive-RAG: learning to adapt retrieval-augmented large language models through question complexity. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 7029\u20137043 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.389"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, Z., et al.: Active retrieval augmented generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7969\u20137992 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.495"},{"key":"13_CR17","unstructured":"Asai, A., Wu, Z., Wang, Y., Sil, A., Hajishirzi, H.: Self-RAG: learning to retrieve, generate, and critique through self-reflection. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"13_CR18","unstructured":"Chan, C.M., et al.: RQ-RAG: learning to refine queries for retrieval augmented generation. arXiv preprint arXiv:2404.00610 (2024)"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Yan, S.Q., Gu, J.C., Zhu, Y., Ling, Z.H.: Corrective retrieval augmented generation. arXiv preprint arXiv:2401.15884 (2024)","DOI":"10.2139\/ssrn.5267341"},{"key":"13_CR20","unstructured":"Edge, D., et al.: From local to global: a graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130 (2024)"},{"key":"13_CR21","unstructured":"Li, Z., et al.: StructRAG: boosting knowledge intensive reasoning of LLMs via inference-time hybrid information structurization. arXiv preprint arXiv:2410.08815 (2024)"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2369\u20132380 (2018)","DOI":"10.18653\/v1\/D18-1259"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Ho, X., Nguyen, A.K.D., Sugawara, S., Aizawa, A.: Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6609\u20136625 (2020)","DOI":"10.18653\/v1\/2020.coling-main.580"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: TriviaQA: a large scale distantly supervised challenge dataset for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601\u20131611 (2017)","DOI":"10.18653\/v1\/P17-1147"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., Hajishirzi, H.: When not to trust language models: investigating effectiveness of parametric and non-parametric memories. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9802\u20139822 (2023)","DOI":"10.18653\/v1\/2023.acl-long.546"},{"key":"13_CR26","unstructured":"Izacard, G., et al.: Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118 (2021)"},{"key":"13_CR27","unstructured":"Yang, A., et\u00a0al.: Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 (2024)"},{"key":"13_CR28","unstructured":"Touvron, H., et\u00a0al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"13_CR29","unstructured":"Dubey, A., et\u00a0al.: The Llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3349-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:50:07Z","timestamp":1763196607000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3349-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"ISBN":["9789819533480","9789819533497"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3349-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"16 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2025\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}