Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding, as one-off explanations may occasionally fall short in providing sufficient information to the user. Current solutions for dialogue-based explanations, however, require many dependencies 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 all explanations by themselves and take care of intent recognition without fine-tuning, by connecting them with a broad spectrum of Explainable AI (XAI) tools, e.g. feature attributions, embedding-based similarity, and prompting strategies for counterfactual and rationale generation. LLM (self-)explanations are presented as an interactive dialogue that supports follow-up questions and generates suggestions. LLMCheckup provides tutorials for operations available in the system, catering to individuals with varying levels of expertise in XAI and supports multiple input modalities. We introduce a new parsing strategy called multi-prompt parsing substantially enhancing the parsing accuracy of LLMs. Finally, we showcase the tasks of fact checking and commonsense question answering.

翻译：可解释性工具以对话形式提供解释，已证明能有效提升用户理解，因为一次性解释有时无法为用户提供足够信息。然而，当前基于对话的解释方案依赖众多组件，且难以迁移至其设计目标以外的任务。我们提出LLMCheckup这一便捷工具，允许用户与任何最先进的大型语言模型就其行为进行对话。通过将LLM与广泛的可解释人工智能工具（例如特征归因、基于嵌入的相似性、以及用于反事实和理由生成的提示策略）相连接，我们使LLM能够自行生成所有解释，并在无需微调的情况下处理意图识别。LLM（自我）解释以支持后续提问和生成建议的交互式对话形式呈现。LLMCheckup为系统可用操作提供教程，面向不同可解释人工智能专业水平的用户，并支持多种输入模态。我们提出一种名为多提示解析的新解析策略，显著提升了LLM的解析准确率。最后，我们展示了事实验证和常识问答任务的应用案例。