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arXiv · 2610.16857

Towards Cost-Efficient Video Dataset Construction with Large Language Models

Mohammed Shabot·Jonathan Adams·Jan Van den Stock·Joost Vennekens
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Abstract

Research on video classification models has witnessed significant growth recently, accompanied by a significant increase in the demand for datasets to train such models. Existing video dataset curation approaches share a common bottleneck: candidate videos must be downloaded before any relevance judgment is made, which results in storage, time and annotation costs. In this work, we present a filtering step, using Large Language Models (LLMs) to pre-screen videos based on their metadata, before actually downloading these videos, thereby saving on these costs. We evaluate the LLMs against a strict human majority vote derived from four independent annotators and validate the reliability of the annotations through inter-annotator agreement analysis (Gwet's AC1). We compare the LLM pre-filter against two no-LLM baselines, namely two keyword heuristics. Via a Leave-One-Rater-Out (LOO) mechanism and an Add-One-Rater-In (AORI), we evaluate the performance of the LLMs as virtual annotators. Finally, we report cost comparisons relative to existing approaches. To ensure reproducability, we made our implementation available on github.

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Mohammed Shabot, Jonathan Adams, Jan Van den Stock, Joost Vennekens. 2026-10-12. Towards Cost-Efficient Video Dataset Construction with Large Language Models. https://arxiv.org/abs/2610.16857

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