Human-in-the-Loop Nugget Annotation for Accountable LLM-as-a-Judge Evaluations

2026-06-27

AI Summary

Current AI evaluation practices relying on human judgment are susceptible to anchoring effects and lack scalability, limiting their ability to provide reliable quality signals. This work proposes a human–AI collaborative evaluation framework in which humans focus on identifying salient information units—referred to as “nuggets”—and making value judgments, while large language models (LLMs) efficiently match model outputs against these nuggets. The approach integrates human oversight and automated scoring through an interactive annotation tool, a three-stage workflow, and an exportable nugget repository. By structuring human input around discrete, reusable semantic units, the framework substantially improves evaluation consistency, scalability, and accountability, thereby enhancing the reliability of LLM-as-a-Judge paradigms.

Abstract

Evaluating AI/Agentic system outputs reliably requires human judgment, but how one incorporates the human determines whether one gets a real quality signal or expensive theater. The common approaches either accidentally anchor human experts (leading to rubber-stamping) or leave them unsupported in high-variance labeling tasks. We present a prototype annotation tool that implements a different division of labor: humans identify what information matters (nuggets), while LLMs handle high-volume matching of nuggets to system outputs. This plays to each party's strengths while maintaining genuine human oversight. We describe the three-phase workflow, key design decisions, and how exported nugget banks integrate with automated judges.

Problem

• Human-in-the-Loop
• LLM-as-a-Judge
• nugget annotation
• accountable evaluation
• human judgment

Innovation

• Human-in-the-Loop
• Nugget Annotation
• LLM-as-a-Judge
• Accountable Evaluation
• Annotation Workflow