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MLLMs show positional bias in multi-video summarization

By PulseAugur Editorial · [3 sources] ·

Researchers have identified a positional bias in multimodal large language models (MLLMs) when summarizing multiple videos. This bias means the quality of a summary can depend on the order in which videos are presented to the model. A new benchmark was created using ActivityNet and News videos to test this effect across nine different MLLMs, revealing that the bias is influenced by both the video domain and the specific model used. The study suggests that current multi-video summarization systems are sensitive to input order, highlighting the need for more robust, order-invariant multimodal systems. AI

IMPACT Highlights a key limitation in current MLLMs for multi-video tasks, pushing for more robust and order-invariant multimodal systems.

RANK_REASON The cluster contains an academic paper detailing a systematic evaluation of a specific AI model behavior.

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AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

MLLMs show positional bias in multi-video summarization

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COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Huangchen Xu, Yuan Wu, Yi Chang ·

    A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs

    arXiv:2606.04596v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quali…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs

    Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the vi…

  3. arXiv cs.CL TIER_1 English(EN) · Yi Chang ·

    A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs

    Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the vi…

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