LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model

• URL: http://arxiv.org/abs/2312.17122v4
• Date: Mon, 28 Oct 2024 05:38:29 GMT
• Title: LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model
• Authors: Haitao Jiang, Lin Ge, Yuhe Gao, Jianian Wang, Rui Song,
• Abstract summary: Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics. 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.
• Score: 7.052058110182703
• License:
• Abstract: 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.

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