Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

1. 1Design a dual-LLM Questioner-Solver architecture to generate code translation data
2. 2Incorporate external knowledge from compilers into the LLM pipeline
3. 3Utilize runtime feedback to refine the generated data
4. 4Train LLMs using the generated data to improve code translation performance
5. 5Evaluate the performance of LLMs on low-resource programming domains

AI engineers and researchers working on LLMs can benefit from this approach to improve code translation accuracy, while software engineers can utilize the generated data to develop more efficient programming frameworks

💡 Dual-LLM dialogue-based approach can generate high-quality code translation data, improving LLM performance in low-resource programming domains

💡 Improve LLM code translation with dual-LLM dialogue-based data generation!