# A Definition of Good Explanations and the Challenges Explaining LLM Outputs

Published: 2026-06-16 · Updated: 2026-06-16 · Category: Research

Source page: https://www.sipply.dev/story/a-definition-of-good-explanations-and-the-challenges-226ae3bf · 中文: https://www.sipply.dev/zh/story/a-definition-of-good-explanations-and-the-challenges-226ae3bf

## Summary

A recent paper published on arXiv delves into the complexities of providing good explanations for outputs generated by large language models (LLMs). It proposes a definition of 'good explanations' that hinges on the concept of counterfactual explanations, which are designed to clarify what could have happened under different circumstances. The research emphasizes the need to tailor these explanations to the prior beliefs and understanding of the audience, suggesting that effective communication of AI outputs is not just about clarity but also about relevance to the user's context.

## Key takeaways

- Good explanations for LLM outputs should be based on counterfactual reasoning.
- Understanding the audience's prior beliefs is crucial for effective explanations.
- The paper highlights the challenges faced in making AI outputs comprehensible.

## Why it matters

This research is significant as it addresses a critical aspect of AI communication, which is essential for user trust and effective interaction with AI systems. By focusing on how explanations can be tailored to individual understanding, it opens pathways for improving user experience and AI adoption.

- **Product managers:** For product managers, understanding the nuances of how explanations are perceived can guide the development of more user-friendly AI interfaces, ultimately enhancing customer satisfaction and engagement.
- **Developers:** Developers and engineers can benefit from this research by integrating these principles into their models, ensuring that the outputs are not only accurate but also interpretable and relevant to users' contexts.
- **Students and job seekers:** Students and job-seekers in AI and related fields should recognize the importance of explainability in AI systems, as it is a growing area of interest among employers and a key factor in the responsible deployment of AI technologies.

## Sources

- [A Definition of Good Explanations and the Challenges Explaining LLM Outputs](https://arxiv.org/abs/2606.14838) (arXiv cs.AI, 2026-06-16)

## 中文

### 良好解释的定义及解释大型语言模型输出的挑战

最近在arXiv上发表的一篇论文探讨了为大型语言模型（LLMs）生成的输出提供良好解释的复杂性。它提出了“良好解释”的定义，基于反事实解释的概念，旨在澄清在不同情况下可能发生的事情。研究强调需要根据受众的先前信念和理解来定制这些解释，表明有效的AI输出沟通不仅关乎清晰度，还关乎与用户上下文的相关性。

**要点：**

- LLM输出的良好解释应基于反事实推理。
- 理解受众的先前信念对有效解释至关重要。
- 论文强调了使AI输出易于理解所面临的挑战。

**为什么重要：** 这项研究的重要性在于它解决了AI沟通的一个关键方面，这对用户信任和与AI系统的有效互动至关重要。通过关注如何根据个人理解定制解释，它为改善用户体验和AI采纳开辟了途径。

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