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1. 6 Oct
   Study: LLM prompting beats fine-tuned encoders for app-review emotion tags

   Researchers evaluated large language models for fine-grained, multi-label emotion classification on mobile app reviews and compared encoder fine-tuning, decoder prompting, and imbalance mitigation strategies. Decoder few-shot prompting achieved the best macro-F1 (0.642), while generative augmentation plus weighted loss improved encoders substantially with much lower inference latency.

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