Training LLMs for Code Generation: Data, Evaluation |Keymakr

Discover how to train LLMs for code generation with our comprehensive guide. Explore data annotation, evaluation, and best practices.

In today’s AI world, large language models are becoming powerful tools not only for natural language processing but also for automatic code generation. The ability of models to deliver functional software solutions opens new horizons for developers, accelerating development, testing, and integration. However, the effectiveness of such models largely depends on the quality of training data, evaluation methods, and the application of best practices in their creation and use. A comprehensive approach to preparing software code corpora that includes different programming languages, coding styles,

related reading

• crawshaw - 2025-01-06crawshaw.io
• Here’s how I use LLMs to help me write codesimonwillison.net
• Composer2.pdfcursor.com
• [2203.07814] Competition-Level Code Generation with AlphaCodearxiv.org
• [2501.01257] CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratingsarxiv.org
• LiCoEval: Evaluating LLMs on License Compliance in Code Generationarxiv.org
• 2502.18449arxiv.org
• [2604.01193] Embarrassingly Simple Self-Distillation Improves Code Generationarxiv.org
• LLM Evaluation doesn't need to be complicatedphilschmid.de
• Coding Models Are Doing Too Much | whnrehiew.github.io
• 2025: The year in LLMssimonwillison.net
• [2107.03374] Evaluating Large Language Models Trained on Codearxiv.org