A framework for auditable, type-checked LM subroutines with bandit prompt optimization and self-critique is applied to NEPA public comment processing; the baseline evaluation shows high quote precision but low recall.
Benchmarking llms for real-world applications: From numerical metrics to contextual and qualitative evaluation
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Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing
A framework for auditable, type-checked LM subroutines with bandit prompt optimization and self-critique is applied to NEPA public comment processing; the baseline evaluation shows high quote precision but low recall.