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cloud.google.com·6h ago
AI Research
arXiv CS.AI·12d ago·5 min read
Risk-Controlled Selective LLM Answering by Pricing Label-Free Checks
TL;DR
PriceCheck is a novel approach that optimizes when to serve answers from large language models by using label-free checks, achieving a selective risk below 1.5% while serving 76.1% of answers. This method outperforms existing models in both coverage and accuracy.
✦ Why It Matters
Implement PriceCheck to enhance the accuracy and reliability of your LLM applications today.
Key Takeaways
- Utilize PriceCheck to optimize answer serving from LLMs.
- Implement label-free checks to reduce error rates.
- Fit prices on diverse datasets for better predictions.
- Aim for a selective risk target below 1.5% in applications.
Full Summary
PriceCheck introduces a method for large language models (LLMs) to determine when to provide answers and when to abstain, focusing on minimizing errors. It utilizes label-free checks, such as re-solving problems, to create a set of decision rules, each associated with a 'price' that reflects its accuracy and cost.
By fitting these prices on a small, diverse labeled dataset, PriceCheck predicts the optimal schedule for running checks and stopping, maintaining a selective risk target. The results show that PriceCheck serves an average of 76.1% of answers while keeping the selective risk below 1.5% across multiple test splits.
Compared to other methods, it serves more answers at the same risk level and maintains the lowest error rate. The high correlation (0.97) between predicted and observed coverage indicates the effectiveness of this pricing-based approach.