# A Taxonomy of Ambiguity Types for NLP

arXiv: https://arxiv.org/abs/2403.14072 · HTML: https://mlsanity.com/paper/2403.14072/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference](https://mlsanity.com/paper/2302.00762/) (2023)

## Built on by (most cited first, 7 of 7)

- [REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?](https://mlsanity.com/paper/2505.10872/) (2025)
- [From Disagreement to Understanding: The Case for Ambiguity Detection in NLI](https://mlsanity.com/paper/2507.15114/) (2025)
- [Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy](https://mlsanity.com/paper/2603.03970/) (2026)
- [XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?](https://mlsanity.com/paper/2609.06842/) (2026)
- [Bridging Lexical Ambiguity and Vision: A Mini Review on Visual Word Sense Disambiguation](https://mlsanity.com/paper/2602.01193/) (2026)
- [Visual Word Sense Disambiguation with CLIP through Dual-Channel Text Prompting and Image Augmentations](https://mlsanity.com/paper/2602.06799/) (2026)
- [Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants](https://mlsanity.com/paper/2607.26611/) (2026)

## Abstract (arXiv)

Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may struggle to grasp certain elements of human language understanding because they may not handle ambiguities at the level that humans naturally do in communication. Additionally, different types of ambiguity may serve different purposes and require different approaches for resolution, and we aim to investigate how language models' abilities vary across types. We propose a taxonomy of ambiguity types as seen in English to facilitate NLP analysis. Our taxonomy can help make meaningful splits in language ambiguity data, allowing for more fine-grained assessments of both datasets and model performance.