Tag
This paper presents a computational approach using large language models and RoBERTa to identify manner and result verbs in sentence context, achieving up to 89.6% accuracy. It aims to provide a scalable measurement tool for developmental language research.
This paper introduces MultiSoc-4D, a benchmark for diagnosing instruction-induced label collapse in LLMs annotating Bengali social media. It reveals that LLMs systematically prefer fallback labels, leading to under-detection of minority categories like hate speech and sarcasm.
Researchers use three open-source LLMs to annotate 10,600 persuader turns in the PersuasionForGood corpus with 41 persuasion strategies, finding that strategy categories explain little donation variance and guilt induction significantly lowers donation rates.