LLMs improve with detailed code descriptions but remain insufficient to replace human annotators for security-specific qualitative coding.
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2 Pith papers cite this work. Polarity classification is still indexing.
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bLLMs achieve state-of-the-art results on limited and imbalanced SE sentiment datasets even in zero-shot settings, but fine-tuned sLLMs outperform when ample balanced training data is available.
citing papers explorer
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LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments
LLMs improve with detailed code descriptions but remain insufficient to replace human annotators for security-specific qualitative coding.
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Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models
bLLMs achieve state-of-the-art results on limited and imbalanced SE sentiment datasets even in zero-shot settings, but fine-tuned sLLMs outperform when ample balanced training data is available.