Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914034684526592 |
|---|---|
| author | Da, Longchao Liu, Xiaoou Dai, Jiaxin Cheng, Lu Wang, Yaqing Wei, Hua |
| author_facet | Da, Longchao Liu, Xiaoou Dai, Jiaxin Cheng, Lu Wang, Yaqing Wei, Hua |
| contents | Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into the reliability of LLM's output regarding a question. In this work, we propose a novel framework that quantifies uncertainty in LLM explanations through a reasoning topology perspective. By designing a structural elicitation strategy, we guide the LLMs to frame the explanations of an answer into a graph topology. This process decomposes the explanations into the knowledge related sub-questions and topology-based reasoning structures, which allows us to quantify uncertainty not only at the semantic level but also from the reasoning path. It further brings convenience to assess knowledge redundancy and provide interpretable insights into the reasoning process. Our method offers a systematic way to interpret the LLM reasoning, analyze limitations, and provide guidance for enhancing robustness and faithfulness. This work pioneers the use of graph-structured uncertainty measurement in LLM explanations and demonstrates the potential of topology-based quantification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology Da, Longchao Liu, Xiaoou Dai, Jiaxin Cheng, Lu Wang, Yaqing Wei, Hua Computation and Language Artificial Intelligence Symbolic Computation 68T50, 68T37, 68Q32 I.2.7; I.2.6; I.2.4 Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into the reliability of LLM's output regarding a question. In this work, we propose a novel framework that quantifies uncertainty in LLM explanations through a reasoning topology perspective. By designing a structural elicitation strategy, we guide the LLMs to frame the explanations of an answer into a graph topology. This process decomposes the explanations into the knowledge related sub-questions and topology-based reasoning structures, which allows us to quantify uncertainty not only at the semantic level but also from the reasoning path. It further brings convenience to assess knowledge redundancy and provide interpretable insights into the reasoning process. Our method offers a systematic way to interpret the LLM reasoning, analyze limitations, and provide guidance for enhancing robustness and faithfulness. This work pioneers the use of graph-structured uncertainty measurement in LLM explanations and demonstrates the potential of topology-based quantification. |
| title | Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology |
| topic | Computation and Language Artificial Intelligence Symbolic Computation 68T50, 68T37, 68Q32 I.2.7; I.2.6; I.2.4 |
| url | https://arxiv.org/abs/2502.17026 |