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Designing Efficient Verifiers for Legal Agents
LangChain Labs developed efficient verifiers for legal agents to address the cost barrier in performance evaluation, finding that batching verifiers and using open models can reduce costs by an order of magnitude. Experiments showed that batch verification is cheaper and faster but requires tracking the full rubric, while per-criterion verification offers higher agreement rates but is more expensive. Tuning prompts for verifiers improved performance, with open models offering cost-performance tradeoffs. DeepSeek, a cheaper alternative to frontier models, showed significant cost savings in post-training evaluations.
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