LLM Evaluation: Bias-Reliability Tradeoff Confirmed Across Diverse Conditions

Evaluating large language models (LLMs) effectively involves a complex interplay between how evaluators are coupled, the diversity of strategies they employ, and the reliability of their measurements. Previous research hinted at a fundamental tradeoff, suggesting that these three factors cannot all be maximized simultaneously. This new study significantly expands the empirical evidence, analyzing eleven different evaluator-agent conditions to provide a more robust understanding of this "bias-reliability tradeoff." The findings strongly support the existence of this tradeoff. Conditions where evaluators are loosely coupled tend to exhibit high strategy diversity but suffer from lower measurement reliability. Conversely, tightly coupled evaluators achieve high reliability but at the cost of reduced strategy diversity. The study also noted unusual behavior in GPT-4o conditions, suggesting potential version drift, and released a new benchmark dataset for further evaluator comparison.