Large Language Models (LLMs) have shown their success in language
understanding and reasoning on general topics. However, their capability to
inference based on user-specified structured data and knowledge in corpus-rare
concepts like causal decision-making is still limited. In this work, we explore
the possibility of fine-tuning an open-sourced LLM into LLM4Causal, which can
identify the causal task, execute a corresponding function, and interpret its
numerical results based on users' queries and the provided dataset. Meanwhile,
we propose a data generation process for more controllable GPT prompting and
present two instruction-tuning datasets: (1) Causal-Retrieval-Bench for causal
problem identification and input parameter extraction for causal function
calling and (2) Causal-Interpret-Bench for in-context causal interpretation.
With three case studies, we showed that LLM4Causal can deliver end-to-end
solutions for causal problems and provide easy-to-understand answers. Numerical
studies also reveal that it has a remarkable ability to identify the correct
causal task given a query.

通过对开源大语言模型进行微调，我们提出了 LLM4Causal，它能够识别因果任务、执行相应的函数并解释其数值结果，同时我们还提出了一种数据生成过程，用于更可控的 GPT 提示，并提供了两个指令微调数据集：因果检索基准和因果解释基准。通过三个案例研究，我们展示了 LLM4Causal 能够为因果问题提供端到端的解决方案并提供易于理解的答案。数值研究还显示，它在给定查询时具有寻找正确因果任务的显著能力。