arXiv:2606. 09809v1 Announce Type: new Abstract: AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2602. 18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action.
arXiv:2606. 14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
arXiv:2608. 13840v1 Announce Type: cross Abstract: Audits of generative AI (GenAI) systems often summarize behavior as a reported rate: how often the audited system complies with policy.
arXiv:2606. 07936v1 Announce Type: cross Abstract: Human evaluation plays a critical role in assessing the quality of generated text.
arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.
arXiv:2607. 28801v1 Announce Type: cross Abstract: Benchmark datasets are central to evaluating Large Language Models (LLMs), yet they are typically conceived as monolithic tasks, obscuring substantial variation in the demands of individual samples.