arXiv:2409.05559v2 Announce Type: replace 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 \textsc{Inverse-Forward Abdu
The rapid advancement of LLMs has brought their reasoning capabilities, especially abductive reasoning, into sharper focus, prompting dedicated research into these complex areas.
Improving LLMs' ability for multi-perspective and multi-level causal discovery directly enhances their autonomous reasoning, paving the way for more sophisticated AI applications in complex problem-solving.
This research introduces concrete methods and a specialized dataset for robust causal discovery, moving LLMs beyond mere pattern recognition towards deeper analytical capabilities relevant to real-world issues like pollution and disease.
- · AI researchers
- · LLM developers
- · Environmental monitoring
- · Healthcare diagnostics
- · Traditional statistical causal inference methods
LLMs gain enhanced capabilities in identifying complex, multi-layered causes of events.
This improved causal reasoning can lead to more effective AI agents in scientific discovery, anomaly detection, and decision-making.
The ability to trace causes precisely could revolutionize diagnostics, risk assessment, and policy formulation in various critical sectors.
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Read at arXiv cs.AI