arXiv:2510.07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check. Vibe check reflects human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking non-functional instructions that users routinely apply. In this paper, we hypothes
The rapid advancement and widespread adoption of Large Language Models (LLMs) for code generation necessitate more sophisticated evaluation methods that align with user expectations beyond mere functionality.
This development indicates a crucial step towards making LLMs more practically useful and 'human-aligned' in software development, moving beyond basic correctness to embrace non-functional requirements.
Code evaluation for LLMs may shift from purely quantitative pass rates to incorporating qualitative 'vibe check' metrics, influencing how future code generation models are trained and refined.
- · AI developers focused on human-centric code generation
- · Software engineers using LLMs for code
- · Companies offering refined code generation tools
- · LLM developers who prioritize only functional correctness
- · Evaluation benchmarks solely focused on pass@k
LLMs will be increasingly evaluated on their ability to generate 'clean' and 'human-readable' code, not just functional code.
This could lead to a new category of competitive advantage for LLM providers who master human preference alignment in code generation.
The concept of 'vibe check' could extend to other generative AI domains, driving a broader paradigm shift in AI evaluation.
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Read at arXiv cs.LG