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 video's input slot even when the underlying content is unchanged. We construct a benchmark from ActivityNet and News videos, covering Cooking, Domestic, Leisure, and News settings with two- and four-video inputs. We evaluate nine open-source and proprietary MLLMs and measure position effects with three complementary metrics: Coverage, Directional Positional Bias (DPB), and Middle-Edge Gap (MEG). Our results show tha...
| Authors | Huangchen Xu, Yuan Wu, Yi Chang |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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