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About this paper
Eungyeup Kim, Chenchen Gu, Vashisth Tiwari, J. Zico Kolter
A framework for sample-efficient evaluation of extreme reliability in large language models is introduced, utilizing the Cross-Entropy Method to identify and target rare, systematic failures in saturated benchmarks. The approach achieves substantial reductions in required inferences while precisely quantifying minute differences in model reliability, such as 'five-nines' accuracy.
Key insights
- LLM failures are systematic and highly concentrated within specific parameter values in the input space, rather than being random or uniformly distributed.
- Increasing the majority-vote ensemble size (K) in self-consistency decoding further amplifies the concentration of systematic failures, making them easier to target.
- Even in 'saturated' benchmarks where models achieve near-perfect scores, this reliability-focused evaluation can reveal measurable, discriminative differences in models' true reliability.
Summary by alphaXiv.
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