3 papers
cs.LG2026
Prioritizing Repeated LLM Evaluation for Hidden Failure Discovery
Keita Broadwater, Akin Broadwater
Large language models are commonly evaluated by generating a small number of stochastic responses for each prompt in a benchmark. Because inference budgets are limited, this shallo…
cs.AI2026
Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling
Keita Broadwater
Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation across diverse tasks. However,…
cs.LG2026
Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing
Keita Broadwater
Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation across diverse tasks and risk categ…