How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators

2025-02-10

AI Summary

Human preference annotations for aligning large language models (LLMs) suffer from heterogeneous quality, difficulty in reliable evaluation, and insufficient annotator incentives. Method: We propose a novel annotator evaluation and incentive framework grounded in principal-agent theory, introducing a continuous action space to model fine-grained annotation behaviors—departing from conventional discrete-action assumptions. We develop a verifiable annotation quality assessment method and design a win-win bonus scheme. Contribution/Results: We theoretically characterize the convergence rate of the optimality gap between binary and linear reward contracts. We prove that, asymptotically, the linear contract achieves Θ(1/n) approximation error to the first-best solution—substantially outperforming the binary contract. Empirical validation, integrating statistical learning theory with real-world preference annotation data, provides both theoretical foundations and practical guidelines for high-quality alignment data production.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design
• Machine Learning: Learning Preferences or Rankings

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we investigate the questions of assessing the performance of human annotators and incentivizing them to provide high-quality annotations. The quality assessment of language/text annotation faces two challenges: (i) the intrinsic heterogeneity among annotators, which prevents the classic methods that assume the underlying existence of a true label; and (ii) the unclear relationship between the annotation quality and the performance of downstream tasks, which excludes the possibility of inferring the annotators' behavior based on the model performance trained from the annotation data. Then we formulate a principal-agent model to characterize the behaviors of and the interactions between the company and the human annotators. The model rationalizes a practical mechanism of a bonus scheme to incentivize annotators which benefits both parties and it underscores the importance of the joint presence of an assessment system and a proper contract scheme. From a technical perspective, our analysis extends the existing literature on the principal-agent model by considering a continuous action space for the agent. We show the gap between the first-best and the second-best solutions (under the continuous action space) is of $Theta(1/sqrt{n log n})$ for the binary contracts and $Theta(1/n)$ for the linear contracts, where $n$ is the number of samples used for performance assessment; this contrasts with the known result of $exp(-Theta(n))$ for the binary contracts when the action space is discrete. Throughout the paper, we use real preference annotation data to accompany our discussions.

Problem

• Assessing human annotators' performance
• Incentivizing high-quality annotations
• Modeling company-annotator interactions

Innovation

• Principal-agent model for annotators
• Continuous action space analysis
• Bonus scheme for quality incentives