SCOPE Framework Uses Conformal Prediction to Calibrate LLM Judges in Pairwise Evaluations
A recent publication on arXiv introduces SCOPE (Selective Conformal Optimized Pairwise Evaluation), a system designed to adjust an acceptance threshold, ensuring that the error rate for non-abstained judgments stays within a user-defined level α. It features Bidirectional Preference Entropy (BPE), which engages judges by presenting both response sequences to yield a bias-free uncertainty signal. In experiments, BPE surpassed traditional confidence proxies in both calibration and discrimination. SCOPE achieved an empirical false discovery rate ranging from approximately 0.097 to 0.099 at α=0.10, effectively managing error rates. The paper, arXiv:2602.13110, tackles the shortcomings of LLM-based evaluation systems and provides a strategy for calibrating pairwise judges, thereby improving automated evaluations across various domains.
Key facts
- SCOPE is a framework for calibrating acceptance thresholds in LLM pairwise evaluation.
- It ensures the error rate among non-abstained judgments is at most a user-specified α under exchangeability.
- BPE (Bidirectional Preference Entropy) is introduced as a bias-neutral uncertainty signal.
- BPE queries the judge under both response positions and uses order-averaged preference probability.
- BPE outperforms standard confidence proxies in calibration and discrimination across pairwise judging benchmarks.
- SCOPE empirically achieves FDR ≈ 0.097–0.099 at α=0.10.
- SCOPE retains substantial coverage while satisfying the risk bound.
- The paper is available on arXiv as 2602.13110, with announcement type replace-cross.
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