Reproduce “Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology” on OpenResearch

About this paper

Longchao Da, Xiaoou Liu, Jiaxin Dai, Lu Cheng, Yaqing Wang, Hua Wei

Researchers from Arizona State University, University of Illinois Chicago, and Purdue University developed Topo-UQ, a framework that quantifies uncertainty in Large Language Model (LLM) explanations by modeling reasoning processes as logical topologies. This approach reveals inconsistencies in intermediate reasoning steps and identifies distinct reasoning patterns, demonstrating stronger negative correlation with explanation faithfulness compared to semantic-only uncertainty quantification methods.

Key insights

• Quantifying uncertainty through reasoning topologies provides a more nuanced and granular understanding of LLM reliability, moving beyond traditional semantic-only or unstructured explanation-based methods.
• LLMs exhibit distinct reasoning patterns, categorized as completion-based, forward reasoning, and combination + verify, which offer insights into their diverse cognitive strategies during problem-solving.
• The effectiveness of the topology-based uncertainty quantification is closely tied to the LLM's instruction-following abilities, as these influence the quality of the elicited reasoning topologies.

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