A Definition of Good Explanations and the Challenges Explaining LLM Outputs

Computer Science > Artificial Intelligence [Submitted on 12 Jun 2026] A Definition of Good Explanations and the Challenges Explaining LLM Outputs Louis Mahon, Elliot Ford, Callum Hackett How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs. Explainability is crucial for AI adoption in many contexts, but in order to produce good explanations of AI systems, we must first have an understanding of what good explanations are. In this paper we propose a definition inspired by the notion of counterfactual explanations, however we argue that one must also take into account the interlocutor's prior beliefs in each fact that could be offered in an explanation. We explore the ramifications of this definition for AI explainability and, in particular, why LLM outputs are difficult to produce good explanations for. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.14838 [cs.AI]   (or arXiv:2606.14838v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.14838 Focus to learn more Submission history From: Louis Mahon [view email] [v1] Fri, 12 Jun 2026 17:11:27 UTC (39 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)