Summary of Multimodal Needle in a Haystack: Benchmarking Long-context Capability Of Multimodal Large Language Models, by Hengyi Wang et al.
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
by Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 The paper introduces the MultiModal Needle-in-a-haystack (MMNeedle) benchmark to evaluate the long-context capabilities of Multimodal Large Language Models (MLLMs). MMNeedle assesses MLLMs by asking them to locate a target sub-image (“needle”) within a set of images (“haystack”) based on textual instructions and descriptions. This requires an understanding of extensive visual contexts and effective information retrieval within long-context image inputs. The authors evaluate state-of-the-art MLLMs, including GPT-4o, and find that it consistently outperforms other models in long-context scenarios but suffers from hallucination problems when the needle is not present. The paper also highlights the performance gap between API-based and open-source models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well computer programs can understand images and text together. It’s called the MultiModal Needle-in-a-haystack benchmark. Imagine you have a big pile of pictures, and you need to find one specific picture (the “needle”) based on what it looks like or what it says. The program has to look at all the pictures and figure out which one is the correct one. The paper tests some popular computer programs that can do this task, and it finds that one program, called GPT-4o, does very well but sometimes makes mistakes when the needle isn’t in the haystack. This helps us understand how these programs work and how we can make them better. |
Keywords
» Artificial intelligence » Gpt » Hallucination
