用大模型自动找历史类比，提升决策理解力
Past Meets Present: Creating Historical Analogy with Large Language Models
- 基于大模型检索与生成结合，挖掘历史事件类比
- 自省机制显著减少幻觉和刻板印象，提升准确性
- 适合历史研究、政策分析及AI辅助决策场景
历史类比是将已知的历史事件与当前陌生事件相比较的重要能力，有助于人们做出判断和理解世界。然而，应用历史研究显示，人类在寻找恰当类比时存在困难；而此前人工智能领域对此关注不足。本文聚焦于历史类比获取任务，旨在为给定事件找到相应的历史类比。我们探索了基于不同大语言模型的检索与生成方法，并提出一种自省机制以缓解大模型生成历史类比时出现的幻觉与刻板印象问题。通过人工评估及专设的多维度自动评估，我们发现大模型在历史类比任务上具有良好潜力，且使用自省方法后性能可进一步提升。
原文摘要 · Abstract (English)
Historical analogies, which compare known past events with contemporary but unfamiliar events, are important abilities that help people make decisions and understand the world. However, research in applied history suggests that people have difficulty finding appropriate analogies. And previous studies in the AI community have also overlooked historical analogies. To fill this gap, in this paper, we focus on the historical analogy acquisition task, which aims to acquire analogous historical events for a given event. We explore retrieval and generation methods for acquiring historical analogies based on different large language models (LLMs). Furthermore, we propose a self-reflection method to mitigate hallucinations and stereotypes when LLMs generate historical analogies. Through human evaluations and our specially designed automatic multi-dimensional assessment, we find that LLMs generally have a good potential for historical analogies. And the performance of the models can be further improved by using our self-reflection method.
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