Quantization and distillation effects on code LLMs (arxiv.org)

A recent study titled "Compressed code: the hidden effects of quantization and distillation on programming tokens" investigates how optimization techniques impact the performance of Large Language Models (LLMs) in code generation tasks. The research highlights the underexplored token-level mechanisms within compressed models, analyzing programming language token representations to understand the vocabulary distribution and keyword coverage. A novel cold-start probability analysis method is introduced, allowing insights into model behavior without specific prompts, enhancing the evaluation process for developing more robust coding applications. The significance of this research lies in its empirical assessment of how model optimizations like quantization, distillation, and fine-tuning influence the quality of code generation. The findings provide valuable, data-driven guidelines for maintaining code generation integrity while optimizing models for production use. By advancing the theoretical understanding of LLM code generation and offering practical insights, this study addresses critical gaps in existing literature, setting a groundwork for future innovations in AI-driven programming tools.