ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation
Quick summary
arXiv:2607.00711v2 Announce Type: cross Abstract: Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input requirements, which are frequently ambiguous, incomplete, or underspecified. While LLMs excel at one-shot code synthesis, their ability to proactively clarify intent remains underexplored, as a critical trait for robust software engineering. Existing benchmarks largely overlook this interactive bottleneck, assuming perfectly specified prompts that do not reflect the iterative nature of requirement elicita
Key takeaways
- arXiv:2607.00711v2 Announce Type: cross Abstract: Large Language Models have emerged as programming assistants.
- However, the efficacy of code generation is constrained by the quality of input requirements, which are frequently ambiguous, incomplete, or underspecified.
- While LLMs excel at one-shot code synthesis, their ability to proactively clarify intent remains underexplored, as a critical trait for robust software engineering.
Why it matters
The importance of “ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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