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A simple approach is presented that introduces error supervision into synthetic essay generation by fine-tune an LLM generator on error-annotated texts of the kind commonly used in Grammatical Error Detection (GED).
Overall, continued pretraining on learner writing can improve in-domain AES, but its benefits depend on both the proficiency composition of the pretraining data and the underlying encoder architecture, and do not consistently extend to cross-test transfer.
This work introduces PRISM (Peer Review Intelligence via Structured Multi-dimensional assessment), a benchmarking framework that evaluates review quality across four dimensions: Depth of Analysis, Novelty Assessment, Law Identification&Major Issues Prioritization, and Multi-dimensional Constructiveness.
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