Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
This work addresses the underexplored need for comprehensive long-context evaluation in MLLMs, which is crucial for researchers and practitioners developing robust multimodal AI systems, though it is incremental as it focuses on benchmarking rather than proposing new methods.
The authors tackled the problem of evaluating long-context capabilities in Multimodal Large Language Models (MLLMs) by introducing the MMNeedle benchmark, which tests their ability to locate target sub-images within large image sets based on textual instructions, and found that GPT-4o outperforms other models but suffers from hallucination issues in negative cases.
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.