Summary of Vlr-bench: Multilingual Benchmark Dataset For Vision-language Retrieval Augmented Generation, by Hyeonseok Lim et al.
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation
by Hyeonseok Lim, Dongjae Shin, Seohyun Song, Inho Won, Minjun Kim, Junghun Yuk, Haneol Jang, KyungTae Lim
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel visual question answering (VQA) benchmark called VLR-Bench to evaluate vision language models (VLMs) that utilize retrieval augmented generation (RAG). Unlike existing datasets, VLR-Bench includes five input passages, allowing for testing of the model’s ability to determine which passage is useful for answering a given query. The authors also introduce a dataset of 32,000 instruction-following examples, dubbed VLR-IF, designed to enhance RAG capabilities in VLMs. They validated the proposed benchmark and training data using state-of-the-art Llama3-based VLM, the Llava-Llama-3 model. The datasets are publicly available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to test computers that can answer questions about images. It’s called VLR-Bench and it has five different pieces of text that help the computer figure out which one is most useful for answering a question. This is important because previous tests only had one piece of text, so this new benchmark helps us see how well these “vision language models” really work. The researchers also created a special dataset with 32,000 examples to help these computers learn how to give good answers. |
Keywords
» Artificial intelligence » Llama » Question answering » Rag » Retrieval augmented generation
