# Reasoning about Intent for Ambiguous Requests

arXiv: https://arxiv.org/abs/2511.10453 · HTML: https://mlsanity.com/paper/2511.10453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 15 of 15)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity](https://mlsanity.com/paper/2510.01171/) (2025)
- [ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification](https://mlsanity.com/paper/2310.10996/) (2023)
- [Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability](https://mlsanity.com/paper/2510.06084/) (2025)
- [BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions](https://mlsanity.com/paper/2510.05318/) (2025)
- [AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL](https://mlsanity.com/paper/2508.15276/) (2025)
- [Asking a Language Model for Diverse Responses](https://mlsanity.com/paper/2509.17570/) (2025)
- [Language Models Identify Ambiguities and Exploit Loopholes](https://mlsanity.com/paper/2508.19546/) (2025)

## Built on by (most cited first, 4 of 4)

- [Act or Clarify? Modeling Sensitivity to Uncertainty and Cost in Communication](https://mlsanity.com/paper/2602.02843/) (2026)
- [Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation](https://mlsanity.com/paper/2605.25831/) (2026)
- [Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?](https://mlsanity.com/paper/2609.07629/) (2026)
- [What Is a Clarification? Estimating Model-Dependent Input Ambiguity](https://mlsanity.com/paper/2610.20052/) (2026)

## Abstract (arXiv)

Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safety risks when that interpretation is wrong. We propose generating a single structured response that enumerates the different ways an ambiguous request can be interpreted, each coupled with a corresponding answer. Our models are trained with reinforcement learning using a dual reward objective: recall on ambiguous inputs to maximise coverage of valid interpretations, and precision on unambiguous ones to suppress spurious alternatives. Training requires only multiple valid answers per input as supervision, no clarification questions or explicit interpretations are needed. Experiments on conversational question answering and semantic parsing demonstrate that our method achieves higher coverage of valid answers than baseline approaches. Human evaluation confirms that predicted interpretations are meaningful and explain their corresponding answers. Our approach promotes transparency with explicit interpretations, avoids additional interaction rounds by producing all interpretations in a single generation, and supports downstream applications through its structured output format.