The paper presents a 300-sample, three-language benchmark with two useful, two partial, and one irrelevant passage per query, together with a training set that improved passage selection in VLMs.
In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online
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VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation
The paper presents a 300-sample, three-language benchmark with two useful, two partial, and one irrelevant passage per query, together with a training set that improved passage selection in VLMs.