Data-Model Co-Evolution: Growing Test Sets to Refine LLM Behavior

• URL: http://arxiv.org/abs/2510.12728v1
• Date: Tue, 14 Oct 2025 17:07:37 GMT
• Title: Data-Model Co-Evolution: Growing Test Sets to Refine LLM Behavior
• Authors: Minjae Lee, Minsuk Kahng,
• Abstract summary: Large Language Models (LLMs) allow developers to govern model behavior by editing prompt instructions.<n>We operationalize this paradigm in an interactive system designed to address the challenge of encoding subtle, domain-specific policies into prompt instructions.
• Score: 10.041741229516141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A long-standing challenge in machine learning has been the rigid separation between data work and model refinement, enforced by slow fine-tuning cycles. The rise of Large Language Models (LLMs) overcomes this historical barrier, allowing applications developers to instantly govern model behavior by editing prompt instructions. This shift enables a new paradigm: data-model co-evolution, where a living test set and a model's instructions evolve in tandem. We operationalize this paradigm in an interactive system designed to address the critical challenge of encoding subtle, domain-specific policies into prompt instructions. The system's structured workflow guides people to discover edge cases, articulate rationales for desired behavior, and iteratively evaluate instruction revisions against a growing test set. A user study shows our workflow helps participants refine instructions systematically and specify ambiguous policies more concretely. This work points toward more robust and responsible LLM applications through human-in-the-loop development aligned with local preferences and policies.

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