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This paper compares human linguists in training, a trained linguist, and LLMs on annotating evaluative language using Appraisal theory, finding that LLMs achieve strong performance and can assist in complex annotation tasks.
GlossAssist is a tool for creating interlinear glossed text (IGT) corpora in low-resource language documentation settings, built around the CWoMP retrieval-based architecture with an active learning feedback loop that improves predictions as annotators make corrections without retraining the model.
This paper evaluates LLMs for automatically annotating narrative macrostructure in spoken Mandarin, finding that the best model achieves near-human reliability while reducing annotation time by 65%, though performance degrades on semantically complex or lexically diverse narratives.