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Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives. A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses. We make three contributions. Firs...
Model comparison increasingly relies on large collections of publicly reported benchmark scores, yet common aggregation strategies trade off evidence coverage against control over capability weighting. Manually curated suites leave potentially informative evaluations unused, while uniform averaging retains them but giv...
Experiments and ablation studies demonstrate that these components significantly improve the evaluation of content relevance, language use, and overall ASA performance, highlighting the benefits of using richer, more nuanced feature sets for holistic speaking assessment.
Balance of Benchmarks (BoB) is introduced, which embeds benchmark descriptions and assigns each benchmark an inverse-density semantic weight, providing a principled foundation for task-aware and multiplicity-robust model evaluation.
Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against an explicit rubric. We test this claim as the design principle behind any-to-bench: a frontier model reads source documents once, at ingestion, to extract each question and its rubri...
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