ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection
📰 ArXiv cs.AI
ProFit leverages high-value signals in supervised fine-tuning via probability-guided token selection to improve Large Language Models alignment with human intent
Action Steps
- Introduce multiple reference answers to mitigate overfitting
- Leverage probability-guided token selection to focus on high-value signals
- Implement ProFit to align LLMs with human intent
- Evaluate ProFit's performance using empirical analysis
Who Needs to Know This
ML researchers and engineers working on Large Language Models can benefit from this approach to improve model performance and reduce overfitting
Key Insight
💡 ProFit mitigates overfitting in SFT by leveraging multiple reference answers and high-value signals
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🚀 ProFit improves LLMs with probability-guided token selection
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