# CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval

arXiv: https://arxiv.org/abs/2405.15784 · HTML: https://mlsanity.com/paper/2405.15784/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [Decomposing Complex Queries for Tip-of-the-tongue Retrieval](https://mlsanity.com/paper/2305.15053/) (2023)
- [Open-domain clarification question generation without question examples](https://mlsanity.com/paper/2110.09779/) (2021)
- [Interactive Classification by Asking Informative Questions](https://mlsanity.com/paper/1911.03598/) (2019)
- [Question Asking as Program Generation](https://mlsanity.com/paper/1711.06351/) (2017)

## Built on by (most cited first, 20 of 20)

- [CollabLLM: From Passive Responders to Active Collaborators](https://mlsanity.com/paper/2502.00640/) (2025)
- [BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design](https://mlsanity.com/paper/2508.21184/) (2025)
- [Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs](https://mlsanity.com/paper/2502.21239/) (2025)
- [Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries](https://mlsanity.com/paper/2411.07237/) (2024)
- [Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization](https://mlsanity.com/paper/2506.06964/) (2025)
- [InfoQuest: Evaluating Multi-Turn Dialogue Agents for Open-Ended Conversations with Hidden Context](https://mlsanity.com/paper/2502.12257/) (2025)
- [Navi-plus: Managing Ambiguous GUI Navigation Tasks with Follow-up Questions](https://mlsanity.com/paper/2503.24180/) (2025)
- [Uncertainty-Aware Clarification in LLM Agents with Information Gain](https://mlsanity.com/paper/2606.03135/) (2026)
- [Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning](https://mlsanity.com/paper/2503.19193/) (2025)
- [Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers](https://mlsanity.com/paper/2601.22139/) (2026)
- [GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare](https://mlsanity.com/paper/2510.08872/) (2025)
- [Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring](https://mlsanity.com/paper/2605.12398/) (2026)
- [Multi-Turn Agentic Scientific Literature Search via Workflow Induction](https://mlsanity.com/paper/2607.00597/) (2026)
- [Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs](https://mlsanity.com/paper/2607.03426/) (2026)
- [Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique](https://mlsanity.com/paper/2608.10430/) (2026)
- [Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation](https://mlsanity.com/paper/2604.03924/) (2026)
- [BALAR : A Bayesian Agentic Loop for Active Reasoning](https://mlsanity.com/paper/2605.05386/) (2026)
- [CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement](https://mlsanity.com/paper/2608.11631/) (2026)
- [Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents](https://mlsanity.com/paper/2609.37236/) (2026)
- [Rational Clarification by Assistive Agents via Value-of-Information Reasoning](https://mlsanity.com/paper/2609.37588/) (2026)

## Abstract (arXiv)

Users often make ambiguous requests that require clarification. We study the problem of asking clarification questions in an information retrieval setting, where systems often face ambiguous search queries and it is challenging to turn the uncertainty in the retrieval model into a natural language question. We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn. When evaluated on a real-world retrieval dataset of users searching for books, our system outperforms traditional heuristics such as information gain on retrieval success by 17% and vanilla-prompted LLMs by 39% relative.