Provable Joint Decontamination for Benchmarking Multiple Large Language Models

TL;DR

This paper introduces JECS, a conformal method for joint decontamination of benchmark data across multiple large language models, ensuring fair evaluation by controlling contamination rates with theoretical guarantees.

Contribution

It formalizes multi-model benchmark decontamination as a joint problem and proposes JECS, a novel conformal procedure with provable global contamination rate control.

Findings

JECS achieves higher power than baseline methods.

JECS maintains target contamination rate control.

Extensive experiments validate JECS's effectiveness.

Abstract

Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audited models, reported performance can be inflated and cross-model comparisons become unreliable. A broad line of training-data detection work designs scores to quantify how strongly a model memorizes a given data point, but these score-based methods lack theoretical guarantees. Recent conformal approaches provide provable false-identification control for a single model; however, applying them separately to each model can produce model-specific benchmarks, undermining fair comparison across models. In this work, we formalize multi-model benchmark decontamination as a joint selection problem and propose Joint Envelope Conformal Selection (JECS), a conformal procedure that enables global contamination rate (GCR) control under stated assumptions. Specifically, JECS computes per-model conformal p-values, aggregates them by the per-item maximum, and reconstructs a conservative envelope of the max-p null distribution from right-tail observations above a data-driven threshold. By applying the adaptive Benjamini-Hochberg (BH) procedure to the envelope-rescaled values, we select a benchmark with provable GCR control. Extensive experiments across various models and benchmarks demonstrate that JECS achieves higher power than the max-p baseline while consistently maintaining the target GCR control.