Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human thinking. Although large language models (LLMs) succeed in many NLP tasks, it is still challenging for them to conduct complex causal reasoning like abductive reasoning and counterfactual reasoning. Given the fact that programming code may express causal relations more often and explicitly with conditional statements like ``if``, we want to explore whether Code-LLMs acquire better causal reasoning abilities. Our experiments show that compared to text-only LLMs, Code-LLMs with code prompts are significantly better in causal reasoning. We further intervene on the prompts from different aspects, and discover that the programming structure is crucial in code prompt design, while Code-LLMs are robust towards format perturbations.
@article{arxiv.2305.19213,
title = {The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code},
author = {Xiao Liu and Da Yin and Chen Zhang and Yansong Feng and Dongyan Zhao},
journal= {arXiv preprint arXiv:2305.19213},
year = {2023}
}
Comments
Findings of ACL 2023. Code and data are available at https://github.com/xxxiaol/magic-if