# Clarifying Ambiguities: on the Role of Ambiguity Types in Prompting Methods for Clarification Generation

arXiv: https://arxiv.org/abs/2504.12113 · HTML: https://mlsanity.com/paper/2504.12113/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 10 of 10)

- [ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models](https://mlsanity.com/paper/2512.21120/) (2025)
- [Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applications](https://mlsanity.com/paper/2511.19933/) (2025)
- [Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks](https://mlsanity.com/paper/2604.14624/) (2026)
- [MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference](https://mlsanity.com/paper/2509.22750/) (2025)
- [Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation](https://mlsanity.com/paper/2603.01375/) (2026)
- [Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy](https://mlsanity.com/paper/2603.03970/) (2026)
- [RAC: Retrieval-Augmented Clarification for Faithful Conversational Search](https://mlsanity.com/paper/2601.11722/) (2026)
- [DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning](https://mlsanity.com/paper/2602.00352/) (2026)
- [Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt](https://mlsanity.com/paper/2604.23263/) (2026)
- [Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA](https://mlsanity.com/paper/2608.03177/) (2026)

## Abstract (arXiv)

In information retrieval (IR), providing appropriate clarifications to better understand users' information needs is crucial for building a proactive search-oriented dialogue system. Due to the strong in-context learning ability of large language models (LLMs), recent studies investigate prompting methods to generate clarifications using few-shot or Chain of Thought (CoT) prompts. However, vanilla CoT prompting does not distinguish the characteristics of different information needs, making it difficult to understand how LLMs resolve ambiguities in user queries. In this work, we focus on the concept of ambiguity for clarification, seeking to model and integrate ambiguities in the clarification process. To this end, we comprehensively study the impact of prompting schemes based on reasoning and ambiguity for clarification. The idea is to enhance the reasoning abilities of LLMs by limiting CoT to predict first ambiguity types that can be interpreted as instructions to clarify, then correspondingly generate clarifications. We name this new prompting scheme Ambiguity Type-Chain of Thought (AT-CoT). Experiments are conducted on various datasets containing human-annotated clarifying questions to compare AT-CoT with multiple baselines. We also perform user simulations to implicitly measure the quality of generated clarifications under various IR scenarios.