LLM4Causal：通过大型语言模型让人人都能使用的大众化因果工具
LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model
AI总结：
本文构建微调开源大模型LLM4Causal，结合两个因果指令数据集，使其能识别任务、调用函数并解释结果，显著提升端到端因果问题求解能力。
摘要：
大型语言模型（LLM）在一般主题的语言理解和推理方面已展现出成功。然而，它们基于用户指定的结构化数据以及语料库中罕见概念的知识进行推理的能力仍然有限，例如因果决策。在本工作中，我们探索了将开源LLM微调为LLM4Causal的可能性，该模型能够根据用户查询和所提供的数据集识别因果任务、执行相应函数并解释其数值结果。同时，我们提出了一种用于更可控的GPT提示的数据生成流程，并给出了两个指令微调数据集：（1）Causal-Retrieval-Bench，用于因果问题识别和因果函数调用的输入参数提取；（2）Causal-Interpret-Bench，用于上下文中的因果解释。通过开展端到端评估和两项消融研究，我们表明LLM4Causal能够为因果问题提供端到端解决方案，并给出易于理解的答案，显著优于基线方法。
英文摘要：
Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics. However, their capability to perform inference based on user-specified structured data and knowledge in corpus-rare concepts, such as 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. By conducting end-to-end evaluations and two ablation studies, we showed that LLM4Causal can deliver end-to-end solutions for causal problems and provide easy-to-understand answers, which significantly outperforms the baselines.
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。
