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Current model testing work has mostly focused on creating test cases.Identifying what to test is a step that is largely ignored and poorlysupported. We propose Weaver, an interactive tool that supports requirementselicitation for guiding model testing. Weaver uses large language models togenerate knowledge bases and recommends concepts from them interactively,allowing testers to elicit requirements for further testing. Weaver providesrich external knowledge to testers and encourages testers to systematicallyexplore diverse concepts beyond their own biases. In a user study, we show thatboth NLP experts and non-experts identified more, as well as more diverseconcepts worth testing when using Weaver. Collectively, they found more than200 failing test cases for stance detection with zero-shot ChatGPT. Our casestudies further show that Weaver can help practitioners test models inreal-world settings, where developers define more nuanced application scenarios(e.g., code understanding and transcript summarization) using LLMs.

Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs