TL;DR
No overall preference is indicated between human and machine-generated summaries; however, experts with greater familiarity with the Bulletin and higher educational attainment showed a marked preference for the official summaries.
Although LLM-based benchmark ratings can approximate expert judgments and reduce the need for labor-intensive human triple coding, limitations remain regarding cost, genre- and task specificity, and sensitivity to text presentation and student grade level—factors that constrain immediate classroom use, particularly for...
In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these systems have been widely adopted by researchers and practitioners across a broad range of measurement tasks, driven by their strong performance, scalability, and cost-eff...
Overall, the findings suggest that LLMs can approximate human coding in this case-specific setting, particularly at the coding level, and may serve as a scalable support tool for inductive qualitative analysis.
The results show that high self-consistency does not necessarily indicate high agreement with human judgments when using local LLMs as automatic judges, and highlight the need to evaluate both consistency and human alignment when using local LLMs as automatic judges.
This work systematically map 14,767 papers introducing or updating evaluation resources from arXiv submissions between January 2022 and August 2026, using staged screening and automated full-text coding to examine changes in target systems and domains, evaluation materials and conditions, and scoring mechanisms.
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented. Validation is challenging, because this is not a task living people ordinarily perform, and because many questions have multiple correct an...
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