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Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
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“As shown in Table 5, some prior studies (Gao et al., 2023;Yu et al., 2023;Zhao et al., 2023) use external knowledge only for selfcorrection, while they can also directly use external knowledge to improve the initial response generation process.”
From When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs (2024) · cites this paper as Zhao et al., 2023
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Language Models are Few-Shot Learners
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ReAct: Synergizing Reasoning and Acting in Language Models
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Measuring and Narrowing the Compositionality Gap in Language Models
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Measuring and Narrowing the Compositionality Gap in Language Models
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Cited by
Sorted by citations
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 4,688
Hallucination is Inevitable: An Innate Limitation of Large Language Models
Xu, Z., Jain, S., & Kankanhalli, M. (2024) · arXiv · doi:10.48550/arxiv.2401.11817 · Cited by 180
Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Kamoi, R., Zhang, Y., Zhang, N., Han, J., & Zhang, R. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00713 · Cited by 200
Zhang, Z., Yao, Y., Zhang, A., Tang, X., Ma, X., He, Z., Wang, Y., Gerstein, M., Wang, R., Liu, G., et al. (2025) · ACM Computing Surveys · doi:10.1145/3719341 · Cited by 51
Metadata
- Authors
- Zhao, R., Li, X., Joty, S., Qin, C., & Bing, L.
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 5823-5840
- Cited by
- 55
- References
- 13
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