Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
<|reference_start|>Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models: Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comprehensive evaluation of their long-context capabilities remains underexplored. To address these gaps, we introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark, specifically designed to assess the long-context capabilities of MLLMs. Besides multi-image input, we employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. Essentially, MMNeedle evaluates MLLMs by stress-testing their capability to locate a target sub-image (needle) within a set of images (haystack) based on textual instructions and descriptions of image contents. This setup necessitates an advanced understanding of extensive visual contexts and effective information retrieval within long-context image inputs. With this benchmark, we evaluate state-of-the-art MLLMs, encompassing both API-based and open-source models. The findings reveal that GPT-4o consistently surpasses other models in long-context scenarios, but suffers from hallucination problems in negative samples, i.e., when needles are not in the haystacks. Our comprehensive long-context evaluation of MLLMs also sheds lights on the considerable performance gap between API-based and open-source models. All the code, data, and instructions required to reproduce the main results are available at https://github.com/Wang-ML-Lab/multimodal-needle-in-a-haystack.<|reference_end|>
@article{wang2024multimodal,
    title={Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of
  Multimodal Large Language Models},
    author={Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu
  Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang},
    journal={arXiv preprint arXiv:2406.11230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11230},
    primaryClass={cs.LG cs.AI cs.CL cs.CV}
}