{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:53:02Z","timestamp":1763196782737,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":27,"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_6","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:49:40Z","timestamp":1763196580000},"page":"68-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DynaRAG: Adaptive Context Compression via\u00a0Reinforcement Learning for\u00a0Enhanced Retrieval-Augmented Generation"],"prefix":"10.1007","author":[{"given":"Yizhuo","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zibo","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"6_CR1","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 (2023)"},{"key":"6_CR2","unstructured":"Borgeaud, S., et\u00a0al.: Improving language models by retrieving from trillions of tokens. In: International Conference on Machine Learning, pp. 2206\u20132240. PMLR (2022)"},{"key":"6_CR3","unstructured":"Cao, J., Jiao, D., Yan, Q., Zhang, W., Tang, S., Zhuang, Y.: Ideal: leveraging infinite and dynamic characterizations of large language models for query-focused summarization. arXiv preprint arXiv:2407.10486 (2024)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Dense X retrieval: what retrieval granularity should we use? In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 15159\u201315177 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.845"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Hallucination detection: robustly discerning reliable answers in large language models. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 245\u2013255 (2023)","DOI":"10.1145\/3583780.3614905"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Duarte, A.V., Marques, J., Gra\u00e7a, M., Freire, M., Li, L., Oliveira, A.L.: LumberChunker: long-form narrative document segmentation. arXiv preprint arXiv:2406.17526 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.377"},{"key":"6_CR7","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2023). 2"},{"key":"6_CR8","unstructured":"He, H., Zhang, H., Roth, D.: Rethinking with retrieval: faithful large language model inference. arXiv preprint arXiv:2301.00303 (2022)"},{"issue":"251","key":"6_CR9","first-page":"1","volume":"24","author":"G Izacard","year":"2023","unstructured":"Izacard, G., et al.: Atlas: few-shot learning with retrieval augmented language models. J. Mach. Learn. Res. 24(251), 1\u201343 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR10","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. arXiv preprint arXiv:2403.14403 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.389"},{"key":"6_CR11","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Are chatGPT and GPT-4 general-purpose solvers for financial text analytics? A study on several typical tasks. arXiv preprint arXiv:2305.05862 (2023)","DOI":"10.18653\/v1\/2023.emnlp-industry.39"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Dong, B., Lin, C., Guerin, F.: Compressing context to enhance inference efficiency of large language models. arXiv preprint arXiv:2310.06201 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.391"},{"key":"6_CR14","unstructured":"Liang, X., et\u00a0al.: Internal consistency and self-feedback in large language models: a survey. arXiv preprint arXiv:2407.14507 (2024)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Z., Yuan, R.: QuerySum: a multi-document query-focused summarization dataset augmented with similar query clusters. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 18725\u201318732 (2024)","DOI":"10.1609\/aaai.v38i17.29836"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Ma, X., Gong, Y., He, P., Zhao, H., Duan, N.: Query rewriting in retrieval-augmented large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 5303\u20135315 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"key":"6_CR17","unstructured":"Shen, X., Chen, Z., Backes, M., Zhang, Y.: In chatGPT we trust? Measuring and characterizing the reliability of chatGPT. arXiv preprint arXiv:2304.08979 (2023)"},{"key":"6_CR18","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":"6_CR19","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: REPLUG: retrieval-augmented black-box language models. arXiv preprint arXiv:2301.12652 (2023)","DOI":"10.18653\/v1\/2024.naacl-long.463"},{"key":"6_CR20","unstructured":"Tang, X., Gao, Q., Li, J., Du, N., Li, Q., Xie, S.: MBA-RAG: a bandit approach for adaptive retrieval-augmented generation through question complexity. arXiv preprint arXiv:2412.01572 (2024)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Yan, S.Q., Gu, J.C., Zhu, Y., Ling, Z.H.: Corrective retrieval augmented generation (2024)","DOI":"10.2139\/ssrn.5267341"},{"key":"6_CR22","unstructured":"Yao, Z., et al.: SeaKR: self-aware knowledge retrieval for adaptive retrieval augmented generation. arXiv preprint arXiv:2406.19215 (2024)"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Yoon, C., Lee, T., Hwang, H., Jeong, M., Kang, J.: Compact: compressing retrieved documents actively for question answering. arXiv preprint arXiv:2407.09014 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.1194"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Yue, Y., Li, Y., Zhan, J.A., Gao, Y.: Query focused summarization via relevance distillation. Neural Comput. Appl. 35(22), 16543\u201316557 (2023)","DOI":"10.1007\/s00521-023-08525-w"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Zhuang, S., Liu, B., Koopman, B., Zuccon, G.: Open-source large language models are strong zero-shot query likelihood models for document ranking. arXiv preprint arXiv:2310.13243 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.590"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zuccon, G., Koopman, B.: Dr chatGPT, tell me what i want to hear: how prompt knowledge impacts health answer correctness. arXiv preprint arXiv:2302.13793 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.928"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Zuccon, G., Koopman, B., Shaik, R.: ChatGPT hallucinates when attributing answers. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 46\u201351 (2023)","DOI":"10.1145\/3624918.3625329"}],"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_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T08:49:46Z","timestamp":1763196586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3349-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"ISBN":["9789819533480","9789819533497"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3349-7_6","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"}}]}}