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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
TL;DR
A new framework for evaluating large language models (LLMs) is introduced, treating the evaluation as a tensor completion problem. This approach enhances uncertainty quantification and improves the reliability of pairwise human judgments in LLM assessments.
✦ Why It Matters
Implement this tensor completion framework to enhance the reliability of your LLM evaluation processes today.
Key Takeaways
- Adopt tensor completion methods for LLM evaluations.
- Utilize the derived one-step debiased estimator for better accuracy.
- Implement score-whitening to stabilize inference results.
- Focus on uncertainty quantification in pairwise comparison data.
How It Works
The framework treats LLM evaluation as a tensor completion problem, leveraging low-rank structures to model pairwise comparisons. By deriving the efficient influence function and semiparametric efficiency bounds, the method allows for improved estimation of model performance metrics, such as ability gaps and win probabilities.
The introduction of score-whitening equalizes local Fisher information, enhancing the stability of inference.