Zero-shot Causal Graph Extrapolation from Text via LLMs
CoRR(2023)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要