From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs
Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real-world usefulness. Instead, users often rely on ``vibe-testing'': informal experience-based evaluation, such as comparing models on coding tasks related to their own workflow. While prevalent, vibe-testing is often too ad hoc and unstructured to analyze or reproduce at scale. In this work, we study how vibe-testing works in practice and then formalize it to support systematic analysis. We first analyze two empirical resources: (1) a survey of user evaluation practices, and (2) a collection of in-the-wild model comparison reports from blogs and social media. Based on these resources, we formalize vibe-tes...
| Authors | Itay Itzhak, Eliya Habba, Gabriel Stanovsky et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 April 2026 |
| Last updated at source | 5 August 2026 |
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