Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models

• URL: http://arxiv.org/abs/2406.11230v1
• Date: Mon, 17 Jun 2024 05:54:06 GMT
• Title: Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
• Authors: Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang,
• Abstract summary: We introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark to assess the long-context capabilities of MLLMs. We employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. We evaluate state-of-the-art MLLMs, encompassing both API-based and open-source models.
• Score: 10.41857522464292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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