用分步提示让大模型从相关性推断因果关系
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
- 将因果发现拆解为固定步骤，逐个提问引导模型
- 在5个大模型上提升因果推理准确率，对变量名变化鲁棒
- 适合需要可解释因果推理的AI应用开发者
大语言模型的推理能力日益受到关注。本文聚焦因果推理，解决基于相关性建立因果关系这一难题——现有多个大模型在此任务上表现不佳。我们提出一种新的提示策略PC-SubQ，将原任务分解为固定子问题，每个子问题对应形式化因果发现算法PC算法的一个步骤。通过逐次提问并把前一步答案加入下一次提示，引导模型按算法流程推理。在现有因果基准Corr2Cause上的实验表明，相较于基线提示策略，该方法在五个大模型上均取得性能提升。结果对因果查询扰动（如变量名修改或表达改写）保持稳定。
原文摘要 · Abstract (English)
The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal relationships based on correlation information, a highly challenging problem on which several LLMs have shown poor performance. We introduce a prompting strategy for this problem that breaks the original task into fixed subquestions, with each subquestion corresponding to one step of a formal causal discovery algorithm, the PC algorithm. The proposed prompting strategy, PC-SubQ, guides the LLM to follow these algorithmic steps, by sequentially prompting it with one subquestion at a time, augmenting the next subquestion's prompt with the answer to the previous one(s). We evaluate our approach on an existing causal benchmark, Corr2Cause: our experiments indicate a performance improvement across five LLMs when comparing PC-SubQ to baseline prompting strategies. Results are robust to causal query perturbations, when modifying the variable names or paraphrasing the expressions.
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