Towards Reliable and Factual Response Generation: Detecting Unanswerable
  Questions in Information-Seeking Conversations
Weronika Łajewska Krisztian Balog
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.