LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
📰 ArXiv cs.AI
Evaluating large language models using tensor completion with low rank structure and semiparametric efficiency
Action Steps
- Formulate LLM evaluation as a tensor completion problem with low rank structure
- Apply semiparametric inference for Bradley-Terry-Luce-type models
- Use pairwise human judgments as noisy and sparse observations
- Quantify uncertainty in leaderboard reporting using the proposed method
Who Needs to Know This
ML researchers and engineers on a team benefit from this approach as it provides a more accurate and efficient way to evaluate LLMs, while data scientists and analysts can apply these methods to improve uncertainty quantification in leaderboard reporting
Key Insight
💡 Tensor completion with low rank structure can efficiently evaluate LLMs using pairwise human judgments
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💡 LLM evaluation as tensor completion with low rank structure and semiparametric efficiency
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