mlsanityData to 7 Oct 2026

CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems

arXiv 2406.08101 ↗First posted 12 Jun 2024cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

20 of 35 references.

References · 20

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8 more
  1. 2024LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanationscited by 16
  2. 2024LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Modelscited by 16
  3. 2022From Black Boxes to Conversations: Incorporating XAI in a Conversational Agentcited by 14
  4. 2023InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanationscited by 11
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandemcited by 8
  2. 2026Do LLM Self-Explanations Help Users Predict Model Behavior? Evaluating Counterfactual Simulatability with Pragmatic Perturbationscited by 7
  3. 2025FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generationcited by 6
  4. 2025Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systemscited by 0
  5. 2026Textual Explanations and Their Evaluations for Reinforcement Learning Policycited by 0