{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:02:08Z","timestamp":1761894128937,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819527243","type":"print"},{"value":"9789819527250","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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\/978-981-95-2725-0_15","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:19:46Z","timestamp":1761887986000},"page":"229-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RJAG: Retrieval Judgment Augmented Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0212-0068","authenticated-orcid":false,"given":"Kuangzhi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1324-1302","authenticated-orcid":false,"given":"Zhenhua","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9759-3918","authenticated-orcid":false,"given":"Min","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6553-376X","authenticated-orcid":false,"given":"Xiangzhi","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"15_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 (2024). https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"15_CR2","unstructured":"Bhakthavatsalam, S., et al.: Think you have solved direct-answer question answering? Try arc-da, the direct-answer AI2 reasoning challenge. arXiv preprint arXiv:2102.03315 (2021)"},{"key":"15_CR3","unstructured":"Ding, H., Pang, L., Wei, Z., Shen, H., Cheng, X.: Retrieve only when it needs: adaptive retrieval augmentation for hallucination mitigation in large language models. arXiv preprint arXiv:2402.10612 (2024)"},{"key":"15_CR4","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey. arXiv preprint arXiv:2312.10997 (2024)"},{"key":"15_CR5","unstructured":"Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M.: Retrieval augmented language model pre-training. In: International Conference on Machine Learning, pp. 3929\u20133938. PMLR (2020)"},{"issue":"2","key":"15_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3703155","volume":"43","author":"L Huang","year":"2025","unstructured":"Huang, L., et al.: A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst. 43(2), 1\u201355 (2025)","journal-title":"ACM Trans. Inf. Syst."},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Jeong, S., Baek, J., Cho, S., Hwang, S.J., Park, J.: 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, pp. 7036\u20137050 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.389"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 6769\u20136781 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Komeili, M., Shuster, K., Weston, J.: Internet-augmented dialogue generation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 8460\u20138478 (2022)","DOI":"10.18653\/v1\/2022.acl-long.579"},{"key":"15_CR10","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Proceedings of the 34th International Conference on Neural Information Processing Systems (2020)"},{"key":"15_CR11","unstructured":"Li, D., et al.: From generation to judgment: opportunities and challenges of LLM-as-a-judge. arXiv preprint arXiv:2411.16594 (2025)"},{"key":"15_CR12","unstructured":"Li, H., Su, Y., Cai, D., Wang, Y., Liu, L.: A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110 (2022)"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Luo, H., et al.: Search augmented instruction learning. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 3717\u20133729 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.242"},{"key":"15_CR14","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, pp. 9802\u20139822 (2023)","DOI":"10.18653\/v1\/2023.acl-long.546"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Min, S., et al.: FActScore: fine-grained atomic evaluation of factual precision in long form text generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 12076\u201312100 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.741"},{"key":"15_CR16","unstructured":"Piktus, A., et al.: The web is your oyster - knowledge-intensive NLP against a very large web corpus. arXiv preprint arXiv:2112.09924 (2022)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Qiao, Z., et al.: Supportiveness-based knowledge rewriting for retrieval-augmented language modeling. In: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 2728\u20132740 (2025)","DOI":"10.18653\/v1\/2025.findings-naacl.148"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Rony, M.R.A.H., Usbeck, R., Lehmann, J.: DialoKG: knowledge-structure aware task-oriented dialogue generation. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 2557\u20132571 (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.195"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Sun, W., et al.: Is ChatGPT good at search? Investigating large language models as re-ranking agents. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 14918\u201314937 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.923"},{"key":"15_CR20","unstructured":"Touvron, H., et al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"15_CR21","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":"15_CR22","unstructured":"Zhang, T., et al.: Interpretable unified language checking. arXiv preprint arXiv:2304.03728 (2023)"},{"key":"15_CR23","unstructured":"Zhang, Y., et al.: Siren\u2019s song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023)"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Zhuang, H., et al.: Beyond yes and no: Improving zero-shot LLM rankers via scoring fine-grained relevance labels. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 358\u2013370 (2024)","DOI":"10.18653\/v1\/2024.naacl-short.31"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-2725-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:20:03Z","timestamp":1761888003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-2725-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819527243","9789819527250"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-2725-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinan","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":"11 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/link.springer.com\/conference\/cncl","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}