mlsanityData to 7 Oct 2026

Best Practices for Managing Data Annotation Projects

arXiv 2009.11654 ↗First posted 24 Sep 2020cs.CY cs.SIcited by 12

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Abstract (arXiv)

Annotation is the labeling of data by human effort. Annotation is critical to modern machine learning, and Bloomberg has developed years of experience of annotation at scale. This report captures a wealth of wisdom for applied annotation projects, collected from more than 30 experienced annotation project managers in Bloomberg's Global Data department.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 3; 2025: 4.

12 papers.

  1. 2023BloombergGPT: A Large Language Model for Financecited by 923
  2. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 9
  3. 2024MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankerscited by 7
  4. 2025LLMs as Span Annotators: A Comparative Study of LLMs and Humanscited by 7
  5. 2025Automatic Labelling with Open-source LLMs using Dynamic Label Schema Integrationcited by 5
  6. 2024Hands-On Tutorial: Labeling with LLM and Human-in-the-Loopcited by 2
  7. 2025Overcoming Language Barriers: Multilingual Analysis of the 2023 Swiss Privacy Law's Impactcited by 1
  8. 2025CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in Chinacited by 1
  9. 2021We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processingcited by 0
  10. 2023Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation Programcited by 0
  11. 2023Abusive Span Detection for Vietnamese Narrative Textscited by 0
  12. 2024Designing A Sustainable Marine Debris Clean-up Framework without Human Labelscited by 0