In the news
PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- PPL-Factory selects training data for fine-tuning language models using task-aware perplexity scores and budget constraints, achieving full-data accuracy with only 10% of samples.
- Why it matters
- Engineers fine-tuning large language models on reasoning tasks like math problems need to reduce computational costs without sacrificing downstream performance.
- Watch out
- Results demonstrated on GSM8K and MATH datasets; effectiveness on other task domains and model sizes remains unclear from this paper.
Listen to this summary
- language model
- reasoning
- fine-tun
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