用类比推理分析复杂场景中的不确定性，提升大模型决策能力
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles
- 从复杂语境构建概率化因子画像
- 结合历史相似案例进行类比推理决策
- 适合金融、咨询等需应对不确定性的场景
大语言模型因具备长上下文推理能力，适用于决策任务。然而，在处理包含重复、模糊和犹豫表达的语音转录文本时面临挑战，例如公司财报电话会议中高管虽乐观预测收入增长以安抚投资者，实则对未来盈利存疑。此时，大模型需系统性地纳入这种不确定性。本文提出 extsc{DeFine} 框架，通过从复杂场景中构建概率化因子画像，并融合类比推理，利用相似过往经验指导大模型在新情境中做出关键决策。该框架将不确定性量化与决策融入分离，特别适用于咨询与金融审议等需在不确定性下做决策的领域。
原文摘要 · Abstract (English)
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.
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