@inproceedings{hu-etal-2025-define,
title = "{D}e{F}ine: Decision-Making with Analogical Reasoning over Factor Profiles",
author = "Hu, Yebowen and
Wang, Xiaoyang and
Yao, Wenlin and
Lu, Yiming and
Zhang, Daoan and
Foroosh, Hassan and
Yu, Dong and
Liu, Fei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.238/",
doi = "10.18653/v1/2025.findings-acl.238",
pages = "4587--4603",
ISBN = "979-8-89176-256-5",
abstract = "LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenarios, as they are verbose and include repetition, hedging, and vagueness. E.g., during a company{'}s earnings call, an executive might project a positive revenue outlook to reassure investors, despite uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a modular framework that constructs probabilistic factor profiles from complex scenarios. It then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in new situations. Our framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. This approach is particularly useful in areas such as consulting and financial deliberation, where making decisions under uncertainty is vital."
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<abstract>LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenarios, as they are verbose and include repetition, hedging, and vagueness. E.g., during a company’s earnings call, an executive might project a positive revenue outlook to reassure investors, despite uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a modular framework that constructs probabilistic factor profiles from complex scenarios. It then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in new situations. Our framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. This approach is particularly useful in areas such as consulting and financial deliberation, where making decisions under uncertainty is vital.</abstract>
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%0 Conference Proceedings
%T DeFine: Decision-Making with Analogical Reasoning over Factor Profiles
%A Hu, Yebowen
%A Wang, Xiaoyang
%A Yao, Wenlin
%A Lu, Yiming
%A Zhang, Daoan
%A Foroosh, Hassan
%A Yu, Dong
%A Liu, Fei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F hu-etal-2025-define
%X LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenarios, as they are verbose and include repetition, hedging, and vagueness. E.g., during a company’s earnings call, an executive might project a positive revenue outlook to reassure investors, despite uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a modular framework that constructs probabilistic factor profiles from complex scenarios. It then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in new situations. Our framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. This approach is particularly useful in areas such as consulting and financial deliberation, where making decisions under uncertainty is vital.
%R 10.18653/v1/2025.findings-acl.238
%U https://aclanthology.org/2025.findings-acl.238/
%U https://doi.org/10.18653/v1/2025.findings-acl.238
%P 4587-4603
Markdown (Informal)
[DeFine: Decision-Making with Analogical Reasoning over Factor Profiles](https://aclanthology.org/2025.findings-acl.238/) (Hu et al., Findings 2025)
ACL
- Yebowen Hu, Xiaoyang Wang, Wenlin Yao, Yiming Lu, Daoan Zhang, Hassan Foroosh, Dong Yu, and Fei Liu. 2025. DeFine: Decision-Making with Analogical Reasoning over Factor Profiles. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4587–4603, Vienna, Austria. Association for Computational Linguistics.