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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.01772 |
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| _version_ | 1866913946283278336 |
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| author | Hu, Yebowen Wang, Xiaoyang Yao, Wenlin Lu, Yiming Zhang, Daoan Foroosh, Hassan Yu, Dong Liu, Fei |
| author_facet | Hu, Yebowen Wang, Xiaoyang Yao, Wenlin Lu, Yiming Zhang, Daoan Foroosh, Hassan Yu, Dong Liu, Fei |
| contents | 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 \textsc{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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01772 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeFine: Decision-Making with Analogical Reasoning over Factor Profiles Hu, Yebowen Wang, Xiaoyang Yao, Wenlin Lu, Yiming Zhang, Daoan Foroosh, Hassan Yu, Dong Liu, Fei Computation and Language Artificial Intelligence 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 \textsc{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. |
| title | DeFine: Decision-Making with Analogical Reasoning over Factor Profiles |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01772 |