arXiv:2607. 17466v1 Announce Type: cross Abstract: Multilingual assessment systems commonly rely on translation for scoring and quality-control processes.
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arXiv:2608. 08283v1 Announce Type: cross Abstract: Although large language models can translate some historical languages surprisingly well, their usefulness in digital humanities workflows is limited by the lack of reliable evaluation.
arXiv:2607. 11873v1 Announce Type: cross Abstract: Institutions collect far more open-ended teaching-evaluation feedback than they read.
arXiv:2607. 19101v1 Announce Type: cross Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications.
arXiv:2606. 28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task.
arXiv:2605. 31220v2 Announce Type: replace-cross Abstract: Confidence estimation (CE), i.
arXiv:2608. 13695v1 Announce Type: cross Abstract: Large language model providers routinely cite multilingual safety benchmarks spanning a dozen or more languages as evidence that their models are safe for non-English-speaking users.