A common issue in text-to-SQL evaluations involves gateways returning HTTP 200 OK even when an upstream error occurs, such as rate-limiting or timeouts. This misleads evaluation harnesses into scoring these infrastructure failures as model inaccuracies. The solution involves inspecting the response body for error objects before trusting the HTTP status code, ensuring that actual model performance is accurately measured and infrastructure issues are handled appropriately. AI
IMPACT Ensures more accurate benchmarking of LLM text-to-SQL capabilities by correctly identifying infrastructure errors.
RANK_REASON Article details a specific technical issue and solution for evaluating LLM text-to-SQL capabilities.
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