LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model

Haitao Jiang, Lin Ge, Yuhe Gao, Jianian Wang, Rui Song

Research Area: LMs with tools and code

Keywords: Causal Decision-Making, LLM with Tools, Output Interpretation, Domain-specific Dataset

TL;DR: Regarding an causal-related question, we developed an end-to-end LLM framework to extract information from data, run the specialized tool to solve the causal task, and provide an interpretation based on the function output.

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 knowl- edge 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 cor- responding function, and interpret its numerical results based on users’ queries and the provided dataset. Meanwhile, we propose a data gen- eration 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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Submission Number: 541