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IBM Research
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Sequential testing can cut model evaluation costs by 80% while ensuring reliable results, challenging the status quo of fixed-size benchmarks.
Cross-lingual exploration can unlock hidden knowledge in LLMs, improving factual recall and consistency across 17 languages.
Systematic gaps in AI evaluation reporting are exposed, revealing inconsistencies that hinder reliable comparisons across thousands of models and benchmarks.
Stop re-running full benchmarks: Calibrate new LLM datasets against existing suites with just 100 "anchor" questions and still get highly accurate performance predictions.
General-purpose agents can match the performance of specialized agents across diverse environments without any environment-specific tuning, challenging the need for task-specific engineering.
