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New agentic pipeline optimizes annotation definitions with expert guidance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Goldsmith, an agentic pipeline designed to optimize annotation definitions using a small set of expert-annotated examples. This system treats the annotation definition as a trainable text object, iteratively revising it based on an executable structured loss function. Goldsmith has demonstrated improvements over existing prompt-optimization methods and enhances downstream annotation tasks when integrated with retrieval and human review processes. AI

IMPACT This research could streamline the creation of high-quality datasets for AI model training by improving the efficiency of expert annotation.

RANK_REASON The cluster describes a new research paper detailing a novel agentic pipeline for optimizing annotation definitions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New agentic pipeline optimizes annotation definitions with expert guidance

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The cluster describes a new research paper detailing a novel agentic pipeline for optimizing annotation definitions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yihan Li, Hanyi Zhang, Xiaoxi Jiang, Man Guo ·

    Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness

    arXiv:2610.09489v1 Announce Type: new Abstract: Many annotation projects begin before experts have a stable guideline or enough labels to train a task-specific model. We present Goldsmith, an agentic pipeline that turns a small gold set---expert-annotated calibration examples rep…

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