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In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes.
Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4812–4829, Online. Association for Computational Linguistics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
Earlier work this paper cites.
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2292–2307, Abu Dhabi, United Arab Emirates, Association for Computational Linguistics
Hung-Ting Chen, Michael Zhang, and Eunsol Choi. 2022 · 2022
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, and D Zhou. 2022 · 2022
Earlier work this paper cites.
Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D Zhou. 2022 · 2022
Cited alongside, same era.
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. 2023 · 2023
Cited alongside, same era.
Can LLMs Generate Random Numbers? EvaluatingLLM Sampling in Controlled Domains. In ICML 2023 Workshop: Sampling and Optimization in Discrete Space
Alex Renda, Aspen Hopkins, and Michael Carbin. 2023 · 2023
Cited alongside, same era.
Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
Closest in time.
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. 2023 · 2023
Closest in time.
Context-faithful prompting for large language models. In ArXiv, abs/2303.11315
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023 · 2023
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