CoXQL：用于解析对话式XAI系统中解释请求的数据集
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
AI总结：
针对对话式可解释AI中意图识别训练数据缺乏且领域特定的问题，提出NLP领域首个涵盖31个意图的CoXQL数据集，并通过引入模板验证增强解析方法（MP+），实验证明其优于以往方法且发现多槽位意图仍具挑战性。
摘要：
基于大语言模型（LLMs）的对话式可解释人工智能（ConvXAI）系统引起了自然语言处理（NLP）和人机交互（HCI）研究界的极大兴趣。这类系统能够在对话中回答用户关于解释的问题，具有增强用户理解力并提供更多关于LLMs决策和生成过程信息的潜力。目前可用的ConvXAI系统基于意图识别而非自由聊天，因为这在识别用户意图方面已被证明更精确、更可靠。然而，对于ConvXAI而言，意图识别仍然是一个挑战，因为现有的训练数据很少且领域高度特定，需要将请求映射到广泛的XAI方法上。为了弥补这一空白，我们提出了CoXQL，这是NLP领域中首个用于ConvXAI用户意图识别的数据集，涵盖31个意图，其中7个需要填充多个槽位。随后，我们通过引入模板验证增强了一种现有的解析方法，并在CoXQL上使用不同的解析策略对几个LLMs进行了评估。我们得出结论，改进后的解析方法（MP+）超越了以往方法的性能。我们还发现，具有多个槽位的意图对LLMs来说仍然极具挑战性。
英文摘要：
Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered significant interest from the research community in natural language processing (NLP) and human-computer interaction (HCI). Such systems can provide answers to user questions about explanations in dialogues, have the potential to enhance users' comprehension and offer more information about the decision-making and generation processes of LLMs. Currently available ConvXAI systems are based on intent recognition rather than free chat, as this has been found to be more precise and reliable in identifying users' intentions. However, the recognition of intents still presents a challenge in the case of ConvXAI, since little training data exist and the domain is highly specific, as there is a broad range of XAI methods to map requests onto. In order to bridge this gap, we present CoXQL, the first dataset in the NLP domain for user intent recognition in ConvXAI, covering 31 intents, seven of which require filling multiple slots. Subsequently, we enhance an existing parsing approach by incorporating template validations, and conduct an evaluation of several LLMs on CoXQL using different parsing strategies. We conclude that the improved parsing approach (MP+) surpasses the performance of previous approaches. We also discover that intents with multiple slots remain highly challenging for LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
