Standardized benchmarks that rank LLM performance by rewarding confident correct answers and penalizing expressions of uncertainty create incentives for models to produce confident guesses rather than explicitly indicating uncertainty or saying 'I don't know'.
high
mechanism
Benchmark-driven evaluation criteria influence model behavior by shaping the objectives optimized during fine-tuning and deployment.