Repeating training data up to 4 epochs yields negligible loss increase versus unique data for fixed compute, and a new scaling law accounts for the decaying value of repeated tokens and excess parameters.
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PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.
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Scaling Data-Constrained Language Models
Repeating training data up to 4 epochs yields negligible loss increase versus unique data for fixed compute, and a new scaling law accounts for the decaying value of repeated tokens and excess parameters.
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Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval
PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.