Refining and Reusing Annotation Guidelines for LLM Annotation

• URL: http://arxiv.org/abs/2605.20809v1
• Date: Wed, 20 May 2026 07:03:42 GMT
• Title: Refining and Reusing Annotation Guidelines for LLM Annotation
• Abstract summary: Large Language Models (LLMs) demonstrate remarkable performance on zero-shot annotation tasks.<n>LLMs often struggle with the specialized conventions of gold-standard benchmarks.<n>We propose an iterative moderation framework that simulates the early phases of annotation projects.
• Score: 17.04320576398815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) demonstrate remarkable performance on 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 good potential in effectively refining guidelines, our analysis also reveals substantial room for improvement.

Related papers

• From Hallucination to Scheming: A Unified Taxonomy and Benchmark Analysis for LLM Deception [32.09232866919422]
  We propose a unified taxonomy organized along three complementary dimensions: degree of goal-directedness, object of deception, and mechanism.<n>Applying this taxonomy to 50 existing benchmarks reveals that every benchmark tests fabrication while pragmatic distortion, attribution, and capability self-knowledge remain critically under-covered.
  arXiv  Detail & Related papers  (2026-04-06T15:57:47Z)
• CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling [61.75914342638658]
  CDRRM (Contrast-Driven Reward Model) is a framework for high-quality rubric generation and guided preference judgment.<n>This work offers a scalable, interpretable, and data-efficient path for reward modeling.
  arXiv  Detail & Related papers  (2026-03-09T07:15:23Z)
• Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents [52.14392337070763]
  We introduce CFG-Bench, a new benchmark designed to systematically evaluate fine-grained action intelligence.<n>CFG-Bench consists of 1,368 curated videos paired with 19,562 three-modalities question-answer pairs targeting four cognitive abilities.<n>Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions.
  arXiv  Detail & Related papers  (2025-11-24T02:02:29Z)
• HADSF: Aspect Aware Semantic Control for Explainable Recommendation [4.75127493865044]
  Recent advances in large language models (LLMs) promise more effective information extraction for recommender systems.<n>We propose a two-stage approach that induces a compact, corpus-level aspect vocabulary via adaptive selection and then performs vocabulary-guided, explicitly constrained extraction of structured aspect-opinion triples.<n> Experiments on approximately 3 million reviews spanning 1.5B-70B parameters show that, when integrated into standard rating predictors, HADSF yields consistent reductions in prediction error.
  arXiv  Detail & Related papers  (2025-10-30T20:49:33Z)
• Generative Reasoning Recommendation via LLMs [48.45009951684554]
  Large language models (LLMs) face fundamental challenges in functioning as generative reasoning recommendation models (GRRMs)<n>This work explores how to build GRRMs by adapting pre-trained LLMs, which achieves a unified understanding-reasoning-prediction manner for recommendation tasks.<n>We propose GREAM, an end-to-end framework that integrates three components: Collaborative-Semantic Alignment, Reasoning Curriculum Activation, and Sparse-Regularized Group Policy Optimization.
  arXiv  Detail & Related papers  (2025-10-23T17:59:31Z)
• OneRec-Think: In-Text Reasoning for Generative Recommendation [55.53292983432484]
  OneRec-Think is a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation.<n>Our proposed "Think-Ahead" architecture enables effective industrial deployment on Kuaishou, achieving a 0.159% gain in APP Stay Time.
  arXiv  Detail & Related papers  (2025-10-13T17:20:13Z)
• The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility? [54.18519360412294]
  Large Language Models (LLMs) must balance between rejecting harmful requests for safety and accommodating legitimate ones for utility.<n>This paper presents a Direct Preference Optimization (DPO) based alignment framework that achieves better overall performance.<n>We analyze experimental results obtained from testing DeepSeek-R1 on our benchmark and reveal the critical ethical concerns raised by this highly acclaimed model.
  arXiv  Detail & Related papers  (2025-01-20T06:35:01Z)
• Learning to Refine with Fine-Grained Natural Language Feedback [81.70313509881315]
  We propose looking at refinement with feedback as a composition of three distinct LLM competencies.<n>A key property of the proposed Detect, Critique, Refine ("DCR") method is that the step 2 critique model can give fine-grained feedback about errors.<n>We show that models of different capabilities benefit from refining with DCR on the task of improving factual consistency of document grounded summaries.
  arXiv  Detail & Related papers  (2024-07-02T16:15:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.