LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model

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View a PDF of the paper titled LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model, by Haitao Jiang and 4 other authors

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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.

Submission history

From: Haitao Jiang [view email]
[v1]
Thu, 28 Dec 2023 16:59:06 UTC (5,556 KB)
[v2]
Fri, 29 Dec 2023 21:54:00 UTC (5,557 KB)
[v3]
Fri, 12 Apr 2024 01:30:55 UTC (5,963 KB)
[v4]
Mon, 28 Oct 2024 05:38:29 UTC (6,029 KB)



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