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Scalable Oversight via Partitioned Human Supervision

Ren Yin, Takashi Ishida, Masashi Sugiyama

TL;DR

The paper tackles the challenge of evaluating and training frontier AI systems when ground-truth labels are infeasible due to cross-domain complexity. It proposes partitioned human supervision that yields complementary labels and derives an unbiased top-1 accuracy estimator $A$, along with two practical mixture estimators: inverse-variance weighting (IVW) and a closed-form maximum-likelihood (ML) approach, both supported by finite-sample deviation guarantees. Empirically, the authors validate unbiasedness and variance properties across benchmarks and demonstrate real-world applicability on finance and medical tasks, while also showing that weak complementary signals can guide agentic training to outperform manually designed baselines. This framework enables scalable oversight by leveraging abundant, narrow-expertise judgments to evaluate and improve superhuman AI without requiring ground-truth solutions, with practical impact for model evaluation and automated agent design.

Abstract

As artificial intelligence (AI) systems approach and surpass expert human performance across a broad range of tasks, obtaining high-quality human supervision for evaluation and training becomes increasingly challenging. Our focus is on tasks that require deep knowledge and skills of multiple domains. Unfortunately, even the best human experts are knowledgeable only in a single narrow area, and will not be able to evaluate the correctness of advanced AI systems on such superhuman tasks. However, based on their narrow expertise, humans may provide a weak signal, i.e., a complementary label indicating an option that is incorrect. For example, a cardiologist could state that "this is not related to cardiology,'' even if they cannot identify the true disease. Based on this weak signal, we propose a scalable oversight framework that enables us to evaluate frontier AI systems without the need to prepare the ground truth. We derive an unbiased estimator of top-1 accuracy from complementary labels and quantify how many complementary labels are needed to match the variance of ordinary labels. We further introduce two estimators to combine scarce ordinary labels with abundant complementary labels. We provide finite-sample deviation guarantees for both complementary-only and the mixed estimators. Empirically, we show that we can evaluate the output of large language models without the ground truth, if we have complementary labels. We further show that we can train an AI system with such weak signals: we show how we can design an agentic AI system automatically that can perform better with this partitioned human supervision. Our code is available at https://github.com/R-Yin-217/Scalable-Oversight-via-Human-Partitioned-Supervision.

Scalable Oversight via Partitioned Human Supervision

TL;DR

The paper tackles the challenge of evaluating and training frontier AI systems when ground-truth labels are infeasible due to cross-domain complexity. It proposes partitioned human supervision that yields complementary labels and derives an unbiased top-1 accuracy estimator , along with two practical mixture estimators: inverse-variance weighting (IVW) and a closed-form maximum-likelihood (ML) approach, both supported by finite-sample deviation guarantees. Empirically, the authors validate unbiasedness and variance properties across benchmarks and demonstrate real-world applicability on finance and medical tasks, while also showing that weak complementary signals can guide agentic training to outperform manually designed baselines. This framework enables scalable oversight by leveraging abundant, narrow-expertise judgments to evaluate and improve superhuman AI without requiring ground-truth solutions, with practical impact for model evaluation and automated agent design.

Abstract

As artificial intelligence (AI) systems approach and surpass expert human performance across a broad range of tasks, obtaining high-quality human supervision for evaluation and training becomes increasingly challenging. Our focus is on tasks that require deep knowledge and skills of multiple domains. Unfortunately, even the best human experts are knowledgeable only in a single narrow area, and will not be able to evaluate the correctness of advanced AI systems on such superhuman tasks. However, based on their narrow expertise, humans may provide a weak signal, i.e., a complementary label indicating an option that is incorrect. For example, a cardiologist could state that "this is not related to cardiology,'' even if they cannot identify the true disease. Based on this weak signal, we propose a scalable oversight framework that enables us to evaluate frontier AI systems without the need to prepare the ground truth. We derive an unbiased estimator of top-1 accuracy from complementary labels and quantify how many complementary labels are needed to match the variance of ordinary labels. We further introduce two estimators to combine scarce ordinary labels with abundant complementary labels. We provide finite-sample deviation guarantees for both complementary-only and the mixed estimators. Empirically, we show that we can evaluate the output of large language models without the ground truth, if we have complementary labels. We further show that we can train an AI system with such weak signals: we show how we can design an agentic AI system automatically that can perform better with this partitioned human supervision. Our code is available at https://github.com/R-Yin-217/Scalable-Oversight-via-Human-Partitioned-Supervision.
Paper Structure (37 sections, 4 theorems, 43 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 37 sections, 4 theorems, 43 equations, 6 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Setup and Notations
  4. Estimator from Complementary Labels
  5. Combining Ordinary and Complementary Labels
  6. Inverse Variance Weighted Estimator (IVW)
  7. Closed-Form Maximum-Likelihood Estimator (ML)
  8. Theoretical analysis
  9. Hoeffding and empirical Bernstein bounds for $\widehat{A}_{\mathrm{comp}}$
  10. Mixture estimator bounds via union bound
  11. Experiments
  12. Experiment Setup
  13. Statistical Validation of Complementary and Mixed Estimators
  14. Ablation Study on IVW.
  15. Benchmark Evaluation in Real-World Tasks
  16. ...and 22 more sections

Key Result

Corollary 1

Under the assumption in Eq. (eq:uniform), the estimator is unbiased for $A$, where $K \geq 3$ is the number of choices.

Figures (6)

  • Figure 1: Proposed framework. Tasks beyond human capabilities are routed to a randomly selected domain expert indicates whether the query belongs to their field. A "no" response yields a complementary label, which is then used to evaluate or train an AI system.
  • Figure 3: Accuracy across different benchmarks. Across GPQA, Math-MC, and Medical Abstract, agentic systems guided by weak complementary signals (ADAS and AFlow) consistently outperform manually designed baselines.
  • Figure 4: Estimator sensitivity under weak signals. Detailed comparison of raw complementary accuracy ($q$) and its linear transform (trans) across GPQA, Math-MC, and Medical Abstract. The figure illustrates how the two estimators behave differently depending on task difficulty, with variance amplification explaining when the transform helps or hurts.
  • Figure : (a) Optimal $w$ as a function of the number of samples (normalized by 300).
  • Figure : (a) Optimal $w$ as a function of the number of samples (normalized by 300).
  • ...and 1 more figures

Theorems & Definitions (7)

  • Corollary 1
  • proof
  • Theorem 2
  • proof
  • Proposition 3
  • Theorem 4
  • proof