Past Meets Present: Creating Historical Analogy with Large Language Models

TL;DR

This paper investigates how large language models can be used to acquire and generate historical analogies, proposing methods to improve their accuracy and reduce biases through self-reflection, with promising evaluation results.

Contribution

It introduces a novel focus on historical analogy acquisition with LLMs, including retrieval, generation, and a self-reflection method to mitigate hallucinations and stereotypes.

Findings

LLMs show good potential for historical analogy tasks

Self-reflection improves model performance

Human and automatic evaluations confirm effectiveness

Abstract

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.