Research2h ago
Evaluation Bottlenecks Now Limit Model Progress
High-quality evaluation data is now scarcer than raw compute. Hugging Face argues that current benchmarks are saturated, leading to data contamination and misleading performance gains. This gap forces researchers to build more expensive, human-centric tests. Practitioners must prioritize custom, domain-specific evals over generic leaderboards to verify actual model utility.