Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation

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Generative Active Testing evaluates LLMs efficiently via proxy task adaptation, reducing labeling costs

advanced Published 23 Mar 2026
Action Steps
  1. Identify a proxy task related to the target domain
  2. Adapt the pre-trained LLM to the proxy task
  3. Use the adapted model to generate test samples
  4. Evaluate the LLM's performance on the generated test samples
Who Needs to Know This

ML researchers and engineers benefit from this approach as it enables efficient evaluation of LLMs in specific domains, such as healthcare and biomedicine, without requiring extensive labeling efforts

Key Insight

💡 Proxy task adaptation can significantly reduce labeling costs for LLM evaluation

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🤖 Efficient LLM evaluation via Generative Active Testing! 📊

Key Takeaways

Generative Active Testing evaluates LLMs efficiently via proxy task adaptation, reducing labeling costs

Full Article

Title: Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation

Abstract:
arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the cost of labeling test samples while developing new benchmarks poses a significant challenge, especially when expert annotators are required. Existing frameworks for active sample selection offer limited support for generative Quest
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