# Asking Clarifying Questions Based on Negative Feedback in Conversational Search

arXiv: https://arxiv.org/abs/2107.05760 · HTML: https://mlsanity.com/paper/2107.05760/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

## Built on by (most cited first, 12 of 12)

- [Proactive Conversational Agents with Inner Thoughts](https://mlsanity.com/paper/2501.00383/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Zero-shot Clarifying Question Generation for Conversational Search](https://mlsanity.com/paper/2301.12660/) (2023)
- [Teaching Language Models To Gather Information Proactively](https://mlsanity.com/paper/2507.21389/) (2025)
- [ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation](https://mlsanity.com/paper/2509.21730/) (2025)
- [LLAMAPIE: Proactive In-Ear Conversation Assistants](https://mlsanity.com/paper/2505.04066/) (2025)
- [Conversational Tree Search: A New Hybrid Dialog Task](https://mlsanity.com/paper/2303.10227/) (2023)
- [Query Understanding in the Age of Large Language Models](https://mlsanity.com/paper/2306.16004/) (2023)
- [Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making](https://mlsanity.com/paper/2504.04141/) (2025)
- [PAQA: Toward ProActive Open-Retrieval Question Answering](https://mlsanity.com/paper/2402.16608/) (2024)
- [TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations](https://mlsanity.com/paper/2604.14223/) (2026)
- [Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration](https://mlsanity.com/paper/2608.20357/) (2026)

## Abstract (arXiv)

Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' search intents. This, however, can take significant effort to answer a series of questions starting with "what/why/how". To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about the previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks.