A Definition of Good Explanations and the Challenges Explaining LLM Outputs

The paper presents a new definition of good explanations in the context of AI, particularly focusing on large language models (LLMs). It introduces the concept of counterfactual explanations while emphasizing the importance of considering the interlocutor's prior beliefs when crafting explanations. This work is significant for AI practitioners as it addresses the challenges of explainability in LLM outputs, which is crucial for fostering trust and understanding in AI systems.