Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended towards Large Language Models (LLMs) which are often criticized for their lack of transparency. This extension calls for a significant transformation in XAI methodologies because of two reasons. First, many existing XAI methods cannot be directly applied to LLMs due to their complexity advanced capabilities. Second, as LLMs are increasingly deployed across diverse industry applications, the role of XAI shifts from merely opening the "black box" to actively enhancing the productivity and applicability of LLMs in real-world settings. Meanwhile, unlike traditional machine learning models that are passive recipients of XAI insights, the distinct abilities of LLMs can reciprocally enhance XAI. Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI. We introduce 10 strategies, introducing the key techniques for each and discussing their associated challenges. We also provide case studies to demonstrate how to obtain and leverage explanations. The code used in this paper can be found at: https://github.com/JacksonWuxs/UsableXAI_LLM.

翻译：可解释人工智能（XAI）是指能够为人类提供关于AI模型运作机制的可理解性洞察的技术。近期，XAI的研究重心正向常因缺乏透明度而受到批评的大型语言模型（LLMs）扩展。这一扩展要求XAI方法论进行重大变革，原因有二：第一，现有许多XAI方法因LLMs的复杂性和先进能力而无法直接应用；第二，随着LLMs在多样化行业应用中的广泛部署，XAI的角色正从单纯打开“黑箱”转向在实际场景中主动提升LLMs的生产力和适用性。同时，不同于传统机器学习模型被动接受XAI的洞察，LLMs的独特能力可反向增强XAI。因此，本文通过分析（1）XAI如何惠及LLMs及AI系统，以及（2）LLMs如何推动XAI的进步，在LLMs背景下引入“可用的XAI”概念。我们提出10种策略，介绍每种策略的关键技术并讨论其相关挑战。此外，本文通过案例研究展示了如何获取并利用解释。本文所用代码可在以下地址获取：https://github.com/JacksonWuxs/UsableXAI_LLM。