Papers with ambiguity detection
Semantic Ambiguity Detection in Sentence Classification using Task-Specific Embeddings (2023.acl-industry)
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| Challenge: | ambiguity is a major obstacle to providing services based on sentence classification . authors use similarity in a semantic space to detect ambiguities in training data and scenarios . |
| Approach: | They use similarity in a semantic space to detect ambiguities in service scenarios and training data. |
| Outcome: | The proposed approach can detect ambiguities and debug services. |
CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems (2026.acl-industry)
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Tabinda Sarwar, Farhad Moghimifar, Cong Duy Vu Hoang, Xiaoxiao Ma, Shawn Chang Xu, Fahimeh Saleh, Poorya Zaremoodi, Avirup Sil, Katrin Kirchhoff
| Challenge: | Existing benchmarks assume a single source of ambiguity and rely on user interaction for resolution, overlooking realistic failure modes. |
| Approach: | They propose a framework for automatically generating an NL2SQL benchmark with multi-faceted ambiguities and diverse user behaviors. |
| Outcome: | The proposed framework transforms executable SQL into ambiguous queries with a conversational continuation and schema-level metadata. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)
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| Challenge: | Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging. |
| Approach: | They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention. |
| Outcome: | The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work. |
Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs (2025.emnlp-main)
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| Challenge: | Ambiguity is pervasive in real-world questions, yet large language models often respond with confident answers rather than seeking clarification. |
| Approach: | They show that question ambiguity is linearly encoded in the internal representations of large language models (LLMs) by training linear probes, they identify sparse sets of Ambiguity-Encoding Neurons (AENs) |
| Outcome: | The proposed model outperforms prompting-based and representation-based baselines on ambiguity detection and generalization. |