Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user. Current solutions for dialogue-based explanations, however, often require external tools and modules and are not easily transferable to tasks they were not designed for. With LLMCheckup, we present an easily accessible tool that allows users to chat with any state-of-the-art large language model (LLM) about its behavior. We enable LLMs to generate explanations and perform user intent recognition without fine-tuning, by connecting them with a broad spectrum of Explainable AI (XAI) methods, including white-box explainability tools such as feature attributions, and self-explanations (e.g., for rationale generation). LLM-based (self-)explanations are presented as an interactive dialogue that supports follow-up questions and generates suggestions. LLMCheckupprovides tutorials for operations available in the system, catering to individuals with varying levels of expertise in XAI and supporting multiple input modalities. We introduce a new parsing strategy that substantially enhances the user intent recognition accuracy of the LLM. Finally, we showcase LLMCheckup for the tasks of fact checking and commonsense question answering.

翻译：以对话形式提供解释的可解释性工具已被证明能有效提升用户理解（Slack等，2023；Shen等，2023），因为一次性解释可能无法为用户提供足够信息。然而，现有基于对话的解释方案通常依赖外部工具和模块，且难以迁移至非预设任务。通过LLMCheckup，我们提出一种便捷工具，允许用户与任何最先进的大语言模型（LLM）就其行为进行对话。通过将LLM与广泛的可解释人工智能（XAI）方法（包括特征归因等白盒可解释性工具和用于原理生成的自我解释）相连接，我们使LLM无需微调即可生成解释并执行用户意图识别。基于LLM的（自）解释以支持追问和生成建议的交互式对话形式呈现。LLMCheckup提供系统操作教程，面向XAI领域不同专业水平的用户，并支持多模态输入。我们提出一种新的解析策略，显著提升了LLM的用户意图识别准确率。最后，我们展示了LLMCheckup在事实核查和常识问答任务中的应用。