Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness
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Computer Science > Computation and Language
arXiv:2610.09489 (cs)
[Submitted on 7 Oct 2026]
Title:Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness
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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 representing the intended task boundaries---into a reusable structured annotation definition. Goldsmith treats this definition as a trainable textual object. Candidate definitions are run on the same gold examples and scored with an executable structured loss, while the output schema, formatting, retrieval, repair, judging, and human review remain in an external harness. A large language model (LLM) editor converts the highest-loss failures into textual-gradient revisions, which are accepted only when the measured loss decreases. In prompt-optimization comparisons, Goldsmith improves over direct rewriting, OPRO, APE, and PromptBreeder under matched evaluation protocols. The resulting definition also improves downstream annotation when combined with retrieval, score-based routing, and human review across typed span, pair-level relation, and fixed-trigger event-argument tasks. These results show that scarce expert supervision can support both task-definition learning and scalable annotation.
| Comments: | 20 pages, 4 figures, 11 tables. Accepted to the main conference of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2610.09489 [cs.CL] |
| (or arXiv:2610.09489v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09489
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness, by Yihan Li and 3 other authors
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