Past Meets Present: Creating Historical Analogy with Large Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911082774265856 |
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| author | Li, Nianqi Yuan, Siyu Chen, Jiangjie Liang, Jiaqing Wei, Feng Liang, Zujie Yang, Deqing Xiao, Yanghua |
| author_facet | Li, Nianqi Yuan, Siyu Chen, Jiangjie Liang, Jiaqing Wei, Feng Liang, Zujie Yang, Deqing Xiao, Yanghua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Past Meets Present: Creating Historical Analogy with Large Language Models Li, Nianqi Yuan, Siyu Chen, Jiangjie Liang, Jiaqing Wei, Feng Liang, Zujie Yang, Deqing Xiao, Yanghua Computation and Language Artificial Intelligence 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. |
| title | Past Meets Present: Creating Historical Analogy with Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.14820 |