Log-likelihood vectors establish a consistent KL divergence scale across pretraining, model sizes, seeds, quantization, fine-tuning, and layers, revealing subdiffusive trajectories and early stabilization in Pythia models.
InInternational Conference on Learning Representations
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A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
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Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Log-likelihood vectors establish a consistent KL divergence scale across pretraining, model sizes, seeds, quantization, fine-tuning, and layers, revealing subdiffusive trajectories and early stabilization in Pythia models.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.