Mapping the Evaluation Frontier: An Empirical Survey of the Bias-Reliability Tradeoff Across Eleven Evaluator-Agent Conditions
arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strategy diversity (H), and small-sample measurement reliability (CV(N)) cannot be simultaneously optimized at fixed sample size N. Prior evidence rests on n=5 conditions with complete metrics from a single study. We expand the empirical base to 11 conditions, measuring gamma and H for all 11 (nine with valid weight vectors) and CV(N=5) for seven with sufficient seeds (N >= 5). Five conditions provide
The proliferation of LLMs and their adoption across various applications necessitates robust and reliable evaluation methodologies to understand their capabilities and limitations.
This research provides a more comprehensive empirical foundation for understanding the fundamental trade-offs in LLM evaluation, moving beyond anecdote towards more scientific rigor.
The understanding of bias-reliability trade-offs in LLM evaluation is expanded, offering a more nuanced view for researchers and developers in designing and interpreting evaluation systems.
- · AI researchers
- · LLM developers
- · AI ethics organizations
- · Enterprises deploying LLMs
- · Developers relying on simplistic evaluation metrics
- · Companies with biased evaluation practices
Improved methodologies for evaluating large language models will emerge, leading to more trustworthy AI systems.
Better understanding of evaluation limitations could guide regulatory frameworks, emphasizing the need for transparent and robust testing.
Enhanced evaluation capabilities might accelerate the pace of AI development by providing clearer feedback loops for model improvement.
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Read at arXiv cs.CL