← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1760 · 记录 #1
Refining and Reusing Annotation Guidelines for LLM Annotation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:50
摘要
While Large Language Models (LLMs) demonstrates remarkable zero-shot annotation tasks, they often struggle with the specialized conventions of gold-standard benchmarks. We propose the systematic reuse and refinement of annotation guidelines as an alignment mechanism, introducing an iterative moderation framework that simulates the early phases of annotation projects. We evaluate three hypotheses: (1) the efficacy of guideline integration, (2) the advantage of reasoning-optimized models, and (3) the viability of moderation under minimal supervision. Testing across biomedical NER tasks (NCBI Disease, BC5CDR, BioRED) with three LLM families (GPT, Gemini, DeepSeek), our results empirically confirm all three hypotheses. While the iterative moderation framework shows a good potential in effectively refining guidelines, our analysis also reveals a significant room for improvement.
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记录 #1 · 2026-07 · 最新观测版本
Refining and Reusing Annotation Guidelines for LLM Annotation
While Large Language Models (LLMs) demonstrates remarkable zero-shot annotation tasks, they often struggle with the specialized conventions of gold-standard benchmarks. We propose the systematic reuse and refinement of annotation guidelines as an alignment mechanism, introducing an iterative moderation framework that simulates the early phases of annotation projects. We evaluate three hypotheses: (1) the efficacy of guideline integration, (2) the advantage of reasoning-optimized models, and (3) the viability of moderation under minimal supervision. Testing across biomedical NER tasks (NCBI Disease, BC5CDR, BioRED) with three LLM families (GPT, Gemini, DeepSeek), our results empirically confirm all three hypotheses. While the iterative moderation framework shows a good potential in effectively refining guidelines, our analysis also reveals a significant room for improvement.
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