bibtext: @inproceedings{
  jiang2024llmcausal,
  title={{LLM}4Causal: Democratized Causal Tools for Everyone via Large Language Model},
  author={Haitao Jiang and Lin Ge and Yuhe Gao and Jianian Wang and Rui Song},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=H1Edd5d2JP}
  }
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.
title: LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model
authors: Haitao Jiang Lin Ge Yuhe Gao Jianian Wang Rui Song