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

arXiv: https://arxiv.org/abs/2401.11452 · HTML: https://mlsanity.com/paper/2401.11452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Sparse, Dense, and Attentional Representations for Text Retrieval](https://mlsanity.com/paper/2005.00181/) (2020)
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://mlsanity.com/paper/2205.12854/) (2022)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://mlsanity.com/paper/2101.05667/) (2021)
- [TREC CAsT 2019: The Conversational Assistance Track Overview](https://mlsanity.com/paper/2003.13624/) (2020)
- [Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval](https://mlsanity.com/paper/2010.11915/) (2020)
- [Read + Verify: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1808.05759/) (2018)
- [Conversations with Search Engines: SERP-based Conversational Response Generation](https://mlsanity.com/paper/2004.14162/) (2020)
- [AttSum: Joint Learning of Focusing and Summarization with Neural Attention](https://mlsanity.com/paper/1604.00125/) (2016)
- [Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering](https://mlsanity.com/paper/1902.10236/) (2019)
- [Towards Filling the Gap in Conversational Search: From Passage Retrieval to Conversational Response Generation](https://mlsanity.com/paper/2308.08911/) (2023)
- [Relation Module for Non-answerable Prediction on Question Answering](https://mlsanity.com/paper/1910.10843/) (2019)

## Built on by (most cited first, 3 of 3)

- [ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions](https://mlsanity.com/paper/2410.14567/) (2024)
- [Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice](https://mlsanity.com/paper/2503.04785/) (2025)
- [Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection](https://mlsanity.com/paper/2506.01104/) (2025)

## Abstract (arXiv)

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.