DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning
<|reference_start|>DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning: LLMs are ideal for decision-making due to their ability to reason over long contexts and identify critical factors. However, challenges arise when processing transcripts of spoken speech describing complex scenarios. These transcripts often contain ungrammatical or incomplete sentences, repetitions, hedging, and vagueness. For example, during a company's earnings call, an executive might project a positive revenue outlook to reassure investors, despite significant uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a new framework that constructs probabilistic factor profiles from complex scenarios. DeFine then integrates these profiles with analogical reasoning, leveraging insights from similar past experiences to guide LLMs in making critical decisions in novel situations. Our framework separates the tasks of quantifying uncertainty in complex scenarios and incorporating it into LLM decision-making. This approach is particularly useful in fields such as medical consultations, negotiations, and political debates, where making decisions under uncertainty is vital.<|reference_end|>
@article{hu2024define:,
    title={DeFine: Enhancing LLM Decision-Making with Factor Profiles and
  Analogical Reasoning},
    author={Yebowen Hu, Xiaoyang Wang, Wenlin Yao, Yiming Lu, Daoan Zhang, Hassan
  Foroosh, Dong Yu, Fei Liu},
    journal={arXiv preprint arXiv:2410.01772},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01772},
    primaryClass={cs.CL cs.AI}
}