RAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing Questions

• URL: http://arxiv.org/abs/2410.14567v1
• Date: Fri, 18 Oct 2024 16:11:29 GMT
• Title: RAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing Questions
• Authors: Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang,
• Abstract summary: Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. This paper presents a novel synthetic data generation method to efficiently create a diverse set of context-grounded confusing questions from a given document corpus.
• Score: 52.33835101586687
• License:
• Abstract: Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. However, many natural questions do not have good answers: about 25\% contain false assumptions~\cite{Yu2023:CREPE}, and over 50\% are ambiguous~\cite{Min2020:AmbigQA}. RAG agents need high-quality data to improve their responses to confusing questions. This paper presents a novel synthetic data generation method to efficiently create a diverse set of context-grounded confusing questions from a given document corpus. We conduct an empirical comparative evaluation of several large language models as RAG agents to measure the accuracy of confusion detection and appropriate response generation. We contribute a benchmark dataset to the public domain.

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