error-bootstrapping

This paper investigates fine-tuning small language models (0.6B-20B parameters) to generate syntactically correct MiniZinc models from natural language descriptions, proposing a cross-model error bootstrapping method that achieves up to 98% execution accuracy, though solution accuracy remains limited.