Zero-shot Causal Graph Extrapolation from Text via LLMs

• URL: http://arxiv.org/abs/2312.14670v1
• Date: Fri, 22 Dec 2023 13:14:38 GMT
• Title: Zero-shot Causal Graph Extrapolation from Text via LLMs
• Authors: Alessandro Antonucci, Gregorio Piqu\'e, Marco Zaffalon
• Abstract summary: We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. We extend our approach to extrapolating causal graphs through iterated pairwise queries.
• Score: 50.596179963913045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. Compared to traditional natural language processing and deep learning techniques, LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. This motivates us to extend our approach to extrapolating causal graphs through iterated pairwise queries. We perform a preliminary analysis on a benchmark of biomedical abstracts with ground-truth causal graphs validated by experts. The results are promising and support the adoption of LLMs for such a crucial step in causal inference, especially in medical domains, where the amount of scientific text to analyse might be huge, and the causal statements are often implicit.

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