arXiv:2509.17314v4 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation. Yet testing them on specific tasks remains difficult and costly: many prompts lack ground truths, forcing reliance on human judgments, while existing test adequacy measures typically rely on output uncertainty and thus are only available after full inference. A key challenge is to assess how useful a test input is in a way that reflects the demands of the task, ideally before
The proliferation of LLMs across various applications necessitates robust testing methods, especially as their deployment moves beyond research into critical software systems.
Improving pre-generation test adequacy for LLM inputs is crucial because it can significantly reduce the cost and reliance on human judgment in validating LLM performance.
The ability to assess test input usefulness before full inference allows for more efficient and scalable validation of LLM functionality, leading to faster development cycles and more reliable deployments.
- · Software Developers
- · AI Companies
- · Quality Assurance Sector
- · LLM Integration Specialists
- · Manual Testing Services for LLMs
- · Companies with Poor LLM Validation Strategies
More reliable and efficient deployment of LLM-powered software features.
Accelerated adoption of LLMs in highly regulated or safety-critical domains due to improved testing rigor.
The development of a new subclass of AI tools focused on automated LLM testing and validation, fostering further innovation in AI development pipelines.
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Read at arXiv cs.LG