The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation

Maja Pavlovic, Massimo Poesio

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Large Language Models (LLMs) have emerged as powerful support tools across various natural language tasks and a range of application domains. Recent studies focus on exploring their capabilities for data annotation. This paper provides a comparative overview of twelve studies investigating the potential of LLMs in labelling data. While the models demonstrate promising cost and time-saving benefits, there exist considerable limitations, such as representativeness, bias, sensitivity to prompt variations and English language preference. Leveraging insights from these studies, our empirical analysis further examines the alignment between human and GPT-generated opinion distributions across four subjective datasets. In contrast to the studies examining representation, our methodology directly obtains the opinion distribution from GPT. Our analysis thereby supports the minority of studies that are considering diverse perspectives when evaluating data annotation tasks and highlights the need for further research in this direction.

Original languageEnglish
Title of host publication3rd Workshop on Perspectivist Approaches to NLP, NLPerspectives 2024 at LREC-COLING 2024 - Workshop Proceedings
EditorsGavin Abercrombie, Valerio Basile, Davide Bernardi, Shiran Dudy, Simona Frenda, Lucy Havens, Sara Tonelli
PublisherEuropean Language Resources Association (ELRA)
Pages100-110
ISBN (Electronic)9782493814234
ISBN (Print)9782493814234
Publication statusPublished - 2024
Event3rd Workshop on Perspectivist Approaches to NLP, NLPerspectives 2024 - Torino, Italy
Duration: 21 May 2024 → …

Publication series

Name3rd Workshop on Perspectivist Approaches to NLP, NLPerspectives 2024 at LREC-COLING 2024 - Workshop Proceedings

Conference

Conference3rd Workshop on Perspectivist Approaches to NLP, NLPerspectives 2024
Country/TerritoryItaly
CityTorino
Period21/05/24 → …

Keywords

  • annotation/labelling
  • large language model (llm)
  • representation

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