人人可及的 XAI：大型语言模型能否简化可解释人工智能？
XAI for All: Can Large Language Models Simplify Explainable AI?
- University of Piraeus（比雷埃夫斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于 ChatGPT Builder 构建的定制 LLM“x-[plAIn]”，可按受众知识与兴趣生成易懂的 XAI 解释，提升复杂 AI 技术的可及性。
AI中文摘要：
可解释人工智能（XAI）领域通常面向具有深厚技术背景的用户，这使得非专业人士理解 XAI 方法颇具挑战。本文提出“x-[plAIn]”，这是一种通过使用 ChatGPT Builder 开发的定制大型语言模型（LLM），让更广泛受众更容易接触 XAI 的新方法。我们的目标是设计一个模型，能够为包括商务专业人士和学者在内的不同受众生成清晰、简洁且针对其需求定制的各类 XAI 方法摘要。该模型的关键特性是能够调整解释，以匹配每个受众群体的知识水平和兴趣。我们的方法仍能提供及时的见解，促进最终用户的决策过程。用例研究结果表明，无论采用何种 XAI 方法，该模型都能有效提供易于理解、面向特定受众的解释。这种适应性提升了 XAI 的可及性，弥合了复杂 AI 技术与其实际应用之间的差距。我们的研究结果表明，LLM 在让多样化用户群体更容易理解先进 AI 概念方面具有广阔前景。
英文摘要：
The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents "x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users.
