Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations

• URL: http://arxiv.org/abs/2401.11452v1
• Date: Sun, 21 Jan 2024 10:15:36 GMT
• Title: Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations
• Authors: Weronika {\L}ajewska, Krisztian Balog
• Abstract summary: Generative AI models face the challenge of hallucinations that can undermine users' trust in such systems. We approach the problem of conversational information seeking as a two-step process, where relevant passages in a corpus are identified first and then summarized into a final system response. Specifically, our proposed method employs a sentence-level classifier to detect if the answer is present, then aggregates these predictions on the passage level, and eventually across the top-ranked passages to arrive at a final answerability estimate.
• Score: 16.99952884041096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI models face the challenge of hallucinations that can undermine users' trust in such systems. We approach the problem of conversational information seeking as a two-step process, where relevant passages in a corpus are identified first and then summarized into a final system response. This way we can automatically assess if the answer to the user's question is present in the corpus. Specifically, our proposed method employs a sentence-level classifier to detect if the answer is present, then aggregates these predictions on the passage level, and eventually across the top-ranked passages to arrive at a final answerability estimate. For training and evaluation, we develop a dataset based on the TREC CAsT benchmark that includes answerability labels on the sentence, passage, and ranking levels. We demonstrate that our proposed method represents a strong baseline and outperforms a state-of-the-art LLM on the answerability prediction task.

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