AI Research
Auditing and Repairing LLM-as-Judge Failures in a Production Text-to-SQL Pipeline
Bridging the gap between LLM judgment and human accuracy in automated database query generation.
Production text-to-SQL pipelines often end with an LLM-as-judge whose agreement with human annotators has never actually been measured. When we checked ours, the deployed gpt-4o-mini judge agreed with two-author gold at only Cohen's kappa = 0.04 on a disagreement-enriched set and...
AC
ArXiv CS.CL
Source
2 min
ArXiv CS.CL
AI Analysis
Generated insight by OnAirToday AI
The reliance on LLM-as-judge systems without rigorous validation creates a significant blind spot in production data pipelines. Auditing these models reveals that even high-performing LLMs can exhibit surprisingly low agreement with human standards, necessitating systematic repair strategies to ensure reliability.
Powered by AI • Not a substitute for human analysisMore Insights →
Share
Filed under:AI Research
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.