IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs

Jinwei He; Feng Lu

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arXiv · 2409.05559

IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs

Jinwei He·Feng Lu

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Abstract

Large language models (LLMs) have developed rapidly, and their reasoning capabilities have become a hot research topic. However, there is still limited exploration of abductive reasoning. The multi-perspective and multi-level of causes is one of the core challenges of abductive reasoning, which cannot be solved well by existing methods. We construct a specialized dataset named DeepAbduction, which is designed for tracing the causes of pollution and disease, addressing the lack of datasets in this field. We propose Inverse-Forward Abductive Reasoning (IFAR) framework for LLMs multi-perspective and multi-level abductive reasoning. IFAR is zero-shot and combines generalized backward reasoning with relation-by-relation forward verification. Experimental results show that IFAR achieves an improvement of approximately 40% in the F1 score compared to other methods under mainstream LLMs, while maintaining a balance between recall and precision. Furthermore, IFAR enhances the performance of non-reasoning LLMs to surpass LLMs which have been trained for reasoning, and remains effective when applied to the latter. Code will be released after the acceptance of our work.

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Jinwei He, Feng Lu. 2024-09-09. IFAR: Multi-Perspective and Multi-Level Causal Discovery with LLMs. https://arxiv.org/abs/2409.05559

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