使大型语言模型从相关推断因果的提示策略
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
AI总结：
本研究提出PC-SubQ提示策略，通过将因果发现PC算法分解为固定子问题逐步引导LLM，在Corr2Cause基准上显著提升了五个LLM从相关推断因果的能力且对查询扰动具有鲁棒性。
摘要：
大型语言模型（LLMs）的推理能力正受到越来越多的关注。在本研究中，我们聚焦于因果推理，并致力于解决基于相关信息建立因果关系这一任务，这是一个极具挑战性的问题，多个LLMs在此表现出较差的性能。我们针对该问题引入了一种提示策略，将原始任务分解为固定的子问题，每个子问题对应形式化因果发现算法——PC算法的一个步骤。所提出的提示策略PC-SubQ，通过每次按顺序用单个子问题提示LLM，并将先前子问题的答案增补至下一子问题的提示中，从而引导LLM遵循这些算法步骤。我们在现有的因果基准Corr2Cause上评估了我们的方法：实验表明，与基线提示策略相比，PC-SubQ在五个LLM上均展现出性能提升。当修改变量名称或改写表达式时，结果对因果查询扰动具有鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
