{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:12:18Z","timestamp":1776121938000,"version":"3.50.1"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,7]]},"DOI":"10.1109\/iscslp63861.2024.10800207","type":"proceedings-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T19:11:17Z","timestamp":1734981077000},"page":"436-440","source":"Crossref","is-referenced-by-count":10,"title":["An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought"],"prefix":"10.1109","author":[{"given":"Yuetong","family":"Zhao","sequence":"first","affiliation":[{"name":"Tsinghua University,Speech Processing and Machine Intelligence (SPMI) Lab"}]},{"given":"Hongyu","family":"Cao","sequence":"additional","affiliation":[{"name":"Tsinghua University,Speech Processing and Machine Intelligence (SPMI) Lab"}]},{"given":"Xianyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"TasiTech"}]},{"given":"Zhijian","family":"Ou","sequence":"additional","affiliation":[{"name":"Tsinghua University,Speech Processing and Machine Intelligence (SPMI) Lab"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6507"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/slt54892.2023.10023191"},{"key":"ref3","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. NeurIPS","author":"Sutskever","year":"2014"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"ref5","article-title":"Chain-of-Thought prompting elicits reasoning in large language models","volume-title":"Proc. NeurIPS","author":"Wei","year":"2022"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.824"},{"key":"ref7","article-title":"Training verifiers to solve math word problems","author":"Cobbe","year":"2021","journal-title":"arXiv preprint"},{"key":"ref8","article-title":"ReAct: Synergizing reasoning and acting in language models","volume-title":"Proc. ICLR","author":"Yao","year":"2022"},{"key":"ref9","article-title":"Retrievalaugmented generation for know ledge-intensive NLP tasks","volume-title":"Proc. NeurIPS","author":"Lewis","year":"2020"},{"issue":"251","key":"ref10","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":"ref11","article-title":"Improving language models by retrieving from trillions of tokens","volume-title":"Proc. ICML","author":"Borgeaud","year":"2022"},{"key":"ref12","article-title":"Retrieval augmented language model pre-training","volume-title":"Proc. ICML","author":"Guu","year":"2020"},{"key":"ref13","article-title":"Generalization through memorization: Nearest neighbor language models","volume-title":"Proc. ICLR","author":"Khandelwal","year":"2019"},{"key":"ref14","article-title":"RAFT: Adapting language model to domain specific RAG","author":"Zhang","year":"2024","journal-title":"arXiv preprint"},{"key":"ref15","article-title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","author":"Huang","year":"2023","journal-title":"arXiv preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1259"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1259"},{"key":"ref18","article-title":"DuReader-robust: A chinese dataset towards evaluating robustness and generalization of machine reading comprehension in real-world applications","volume-title":"Proc. ACL","author":"Tang","year":"2021"},{"key":"ref19","article-title":"Qwen technical report","author":"Bai","year":"2023","journal-title":"arXiv preprint"},{"key":"ref20","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv preprint"},{"key":"ref21","article-title":"LLAMA 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023","journal-title":"arXiv preprint"},{"key":"ref22","volume-title":"Dureader robust"},{"key":"ref23","volume-title":"Hotpotqa"}],"event":{"name":"2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP)","location":"Beijing, China","start":{"date-parts":[[2024,11,7]]},"end":{"date-parts":[[2024,11,10]]}},"container-title":["2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10799944\/10799969\/10800207.pdf?arnumber=10800207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T06:33:20Z","timestamp":1735022000000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10800207\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/iscslp63861.2024.10800207","relation":{},"subject":[],"published":{"date-parts":[[2024,11,7]]}}}