# Past Meets Present: Creating Historical Analogy with Large Language Models

arXiv: https://arxiv.org/abs/2409.14820 · HTML: https://mlsanity.com/paper/2409.14820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts](https://mlsanity.com/paper/2305.13300/) (2023)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Can Language Models be Biomedical Knowledge Bases?](https://mlsanity.com/paper/2109.07154/) (2021)
- [How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study](https://mlsanity.com/paper/2402.16061/) (2024)
- [BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?](https://mlsanity.com/paper/2105.04949/) (2021)
- [Distilling Script Knowledge from Large Language Models for Constrained Language Planning](https://mlsanity.com/paper/2305.05252/) (2023)
- [A Theoretical Understanding of Self-Correction through In-context Alignment](https://mlsanity.com/paper/2405.18634/) (2024)
- [E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning](https://mlsanity.com/paper/2203.08480/) (2022)
- [Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT](https://mlsanity.com/paper/2210.04186/) (2022)
- [StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding](https://mlsanity.com/paper/2310.12874/) (2023)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes](https://mlsanity.com/paper/2210.12197/) (2022)
- [Analogies minus analogy test: measuring regularities in word embeddings](https://mlsanity.com/paper/2010.03446/) (2020)
- [ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base](https://mlsanity.com/paper/2305.05994/) (2023)
- [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024)
- [Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction](https://mlsanity.com/paper/2305.12660/) (2023)
- [ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies](https://mlsanity.com/paper/2403.01139/) (2024)
- [Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI](https://mlsanity.com/paper/2401.17856/) (2024)

## Built on by (most cited first, 1 of 1)

- [Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis](https://mlsanity.com/paper/2607.13602/) (2026)

## Abstract (arXiv)

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.