CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation

• URL: http://arxiv.org/abs/2503.22688v1
• Date: Wed, 05 Mar 2025 09:47:02 GMT
• Title: CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
• Authors: Peiding Wang, Li Zhang, Fang Liu, Lin Shi, Minxiao Li, Bo Shen, An Fu,
• Abstract summary: We introduce bench, a benchmark for evaluating Large Language Models' instruction-following capabilities.<n>bench incorporates nine types of verifiable instructions aligned with the real-world software development requirements.<n>We evaluate nine prominent LLMs using bench, and the experimental results reveal a significant disparity between their basic programming capability and instruction-following capability.
• Score: 10.438717413104062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existing code generation benchmarks primarily assess the functional correctness of code generated by LLMs in single-turn interactions, offering limited insight into their capabilities to generate code that strictly follows users' instructions, especially in multi-turn interaction scenarios. In this paper, we introduce \bench, a benchmark for evaluating LLMs' instruction-following capabilities in interactive code generation. Specifically, \bench incorporates nine types of verifiable instructions aligned with the real-world software development requirements, which can be independently and objectively validated through specified test cases, facilitating the evaluation of instruction-following capability in multi-turn interactions. We evaluate nine prominent LLMs using \bench, and the experimental results reveal a significant disparity between their basic programming capability and instruction-following capability, particularly as task complexity, context length, and the number of dialogue rounds increase.

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