# LaViSA: A Language and Vision Structural Ambiguity Benchmark

arXiv: https://arxiv.org/abs/2606.19552 · HTML: https://mlsanity.com/paper/2606.19552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality](https://mlsanity.com/paper/2204.03162/) (2022)
- [Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities](https://mlsanity.com/paper/2210.12187/) (2022)
- [ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs](https://mlsanity.com/paper/2406.08164/) (2024)
- [Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA](https://mlsanity.com/paper/2211.07516/) (2022)
- [Do You See What I Mean? Visual Resolution of Linguistic Ambiguities](https://mlsanity.com/paper/1603.08079/) (2016)
- [A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions](https://mlsanity.com/paper/2403.17545/) (2024)
- [Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!](https://mlsanity.com/paper/2410.01023/) (2024)
- [Does Vision Accelerate Hierarchical Generalization in Neural Language Learners?](https://mlsanity.com/paper/2302.00667/) (2023)

## Abstract (arXiv)

Structural ambiguity arises when a single sentence admits multiple valid interpretations due to its syntactic structure, posing a fundamental challenge for language understanding. Visual scenes serve as useful cues for resolving such ambiguity, and Vision and Language Models (VLMs) need to be capable of deriving possible semantic interpretations from visual scenes. We introduce Language and Vision Structural Ambiguity (LaViSA), a benchmark designed to evaluate the ability of VLMs to resolve structural ambiguity leveraging visual scenes. LaViSA consists of ambiguous sentences, their disambiguated sentences, and corresponding images of these disambiguated sentences across seven ambiguity categories. Using LaViSA, we conduct a comprehensive evaluation of diverse VLMs, including both proprietary and open-source models with varying parameter scales and reasoning capabilities. Experimental results show that although recent VLMs can leverage visual scenes to resolve structural ambiguity to a some extent, they still struggle with certain ambiguity types and visually subtle semantic distinctions, indicating remaining limitations in resolving structural ambiguity using visual scenes.