Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

TL;DR

This paper introduces ten strategies for making explainable AI more effective and usable in the context of large language models, emphasizing their role in improving AI transparency and application.

Contribution

It presents a novel framework of ten strategies for applying and enhancing XAI techniques specifically for LLMs, addressing unique challenges and opportunities.

Findings

Ten key strategies for Usable XAI in LLMs

Case studies demonstrating explanation techniques

Discussion of challenges and solutions in XAI for LLMs

Abstract

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 toward explaining Large Language Models (LLMs). This extension calls for a significant transformation in the XAI methodologies for two reasons. First, many existing XAI methods cannot be directly applied to LLMs due to their complexity and advanced capabilities. Second, as LLMs are increasingly deployed in diverse 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, the conversation and generation 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 explain and improve LLM-based AI systems and (2) how XAI techniques can be improved by using LLMs. 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.