VIDA D
AI benchmark · Translation
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Full listing name: VIDA: A dataset for Visually Dependent Ambiguity in Multimodal Machine Translation
What is VIDA?
Ambiguity resolution is a key challenge in multimodal machine translation (MMT), where models must genuinely leverage visual input to map an ambiguous expression to its intended meaning. Although prior work has proposed disambiguation-oriented benchmarks probing the role of vision, we observe that existing benchmarks remain limited by task-format mismatch, narrow ambiguity coverage, or insufficient visual-dependency validation. Moreover, existing ambiguity evaluations are not well suited to diverse ambiguity types in open-ended translation. To address these limitations, we present VIDA (Visually-Dependent Ambiguity), a dataset of 2,500 carefully curated instances in which resolving an annotated source span requires visual evidence. We further propose Disambiguation-Centric Metrics that use an LLM-as-a-judge classifier to verify whether annotated ambiguous expressions are resolved correctly at the span level. Experiments with two state-of-the-art LVLMs show that supervised fine-tuning (SFT) improves overall translation quality, while chain-of-thought SFT (CoT-SFT) yields stronger out-of-distribution disambiguation, suggesting that explicit disambiguation guidance improves generalization to diverse ambiguity types.
Tier D
Long tail: little or no usage data yet.
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- 2 models scored
- fewer than 5 models scored
- in curated RL directories (1 pt)
How we got to tier D
- Adoption: 2 models scored; fewer than 5 models scored. Tier D.
- Not yet read on X.
- In curated RL directories: 1 point, a floor of none.
- Not yet confirmed by a line from its own site or paper.
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Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-11 snapshot.