LoRA fine-tuning makes compact open-weight LLMs competitive with, and often better than, prompt-only proprietary GPT models for codebook-guided coding of students' math metaphors, while improving run-to-run reliability.
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Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses
LoRA fine-tuning makes compact open-weight LLMs competitive with, and often better than, prompt-only proprietary GPT models for codebook-guided coding of students' math metaphors, while improving run-to-run reliability.