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 2021 Conference on Empirical Methods in Natural Language Processing , pages 6417–6431
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.