C o XQL : A Dataset for Parsing Explanation Requests in Conversational XAI Systems (2024)

Abstract: 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.

## introduction there is an increasing number of xai systems that include user interfaces, facilitating natural language interaction with users (chromik and butz, figure 1 : example utterances consisting of user questions, sql-like queries (parsed texts) and corresponding responses (not included in coxql) for influence (influence), feature attribution (nlpattribute) and rationalization @xcite . more examples and operations can be found in ## related work in the majority of previous convxai systems @xcite @xcite , the semantic similarity of sentence embeddings between user query and existing data is used to match the user query with the appropriate operation ( ## evaluation 5.1 automatic evaluation to assess the ability of interpreting user intents with llms, we quantify the performance of seven llms foot_4 with different sizes ranging from 1b to 70b, employing four approaches: nn, gd, mp and mp+ ( §3.3) ( we find that mp falls short of gd on coxql except codeqwen1.5 @xcite , while improved mp (mp+) can easily outperform gd and mp with additional template checks. among all llms and parsing strategies, our findings reveal that llama3-70b with mp+ demonstrates the highest scores, exhibiting a doubling in performance compared to the baseline (nn). ## conclusion the contributions of this paper are three-fold: firstly, we present and release the first dataset coxql for explanation request parsing in the nlp domain for convxai systems, featuring 31 intents. secondly, we improve the previous parsing strategy mp with additional template checks, which considerably improves parsing accuracy. lastly, we perform a comparative evaluation of seven stateof-the-art llms on the coxql data. we find that mp+ outperforms both gd and mp but llms still struggle with intents that have multiple slots. in the future, we would like to consider tools like langchain foot_6 to provide more accessible, extensible framework.