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

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 \textsc{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.