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arXiv · 2609.22150

ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models

Hyomin Kim·Bowen Chen·Jin Huang·Zhao Wang·Qiaozhu Mei·Shingo Takamatsu
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Abstract

Content promotion spans a broad set of skills, from understanding content to forecasting its market reception. However, LLMs' ability to support such promotion decisions remains underexplored. Existing studies are often limited to a single task (e.g., popularity prediction) or a small set of tasks within a single domain (e.g., movies). As a result, there is a lack of understanding of LLMs' abilities in the full promotion process and how these abilities generalize across different tasks and domains. In this work, we introduce ECP-Bench, a benchmark containing 1.9M movie, game, and music items and 423,451 questions across 33 tasks in five content-promotion skill families. Our evaluation shows that frontier models achieve only 51.9\% overall accuracy and lose much of their advantage on post-cutoff content, with drops of up to 19.1 percentage points. In contrast, open-weight models fine-tuned on ECP-Bench achieve up to 60.3\%, remain substantially more stable across the knowledge cutoff, generalize to unseen content and tasks, and exhibit meaningful cross-domain generalization.

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Hyomin Kim, Bowen Chen, Jin Huang, Zhao Wang, Qiaozhu Mei, Shingo Takamatsu. 2026-08-26. ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models. https://arxiv.org/abs/2609.22150

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