Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.
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Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.