📝 SummarXiv

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, a gap remains between their output and the problem-solving strategies of human developers. Unlike humans, who spend substantial time disambiguating requirements through iterative dialogue, LLMs often generate code despite ambiguities in natural language requirements, leading to unreliable solutions. Different from prior work, we study whether a Code LLM can be fine-tuned to learn clarification-seeking behavior. While recent work has focused on LLM-based agents for iterative code generation, we argue that the ability to recognize and query ambiguous requirements should be intrinsic to the models themselves, especially in agentic AI where models and humans collaborate. We present ClarifyCoder, a framework with synthetic data generation and instruction-tuning that fine-tunes an LLM to identify ambiguities and request clarification before code generation. Our approach has two components: (1) a data synthesis technique that augments programming datasets with scenarios requiring clarification to generate clarification-aware training data, and (2) a fine-tuning strategy that teaches models to prioritize seeking clarification over immediate code generation when faced with incomplete or ambiguous requirements. We also provide an empirical analysis of integrating ClarifyCoder with standard fine-tuning for joint optimization of clarification-awareness and coding ability. Experimental results show that ClarifyCoder achieves a 63% communication rate (40% absolute increase) and a 52% good question rate (30% absolute increase) on ambiguous tasks, significantly improving LLMs' communication capabilities while maintaining code generation performance.