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Is GPT-3 a Good Data Annotator? (2212.10450v2)

Published 20 Dec 2022 in cs.CL

Abstract: Data annotation is the process of labeling data that could be used to train machine learning models. Having high-quality annotation is crucial, as it allows the model to learn the relationship between the input data and the desired output. GPT-3, a large-scale LLM developed by OpenAI, has demonstrated impressive zero- and few-shot performance on a wide range of NLP tasks. It is therefore natural to wonder whether it can be used to effectively annotate data for NLP tasks. In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks. Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP.

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Citations (200)
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Summary

  • The paper demonstrates GPT-3's dual capability to annotate existing data and generate training datasets, achieving near-human accuracy (87.75 vs. 88.47 in SST2) at lower costs.
  • The study introduces three annotation methods—PGDA, PGDG, and DADG—tailored to optimize performance across tasks with varying label complexities.
  • The paper highlights GPT-3’s strength in large-scale, cost-efficient annotation for NLP, while noting potential quality trade-offs and the need for further refinement.

Evaluating the Potential of GPT-3 as a Data Annotator for NLP Tasks

The paper entitled "Is GPT-3 a Good Data Annotator?" presents an empirical investigation into the capability of GPT-3 in the context of data annotation for NLP tasks. The paper centers on assessing the effectiveness, efficiency, and cost-effectiveness of GPT-3 when applied to various NLP tasks, as compared to traditional annotation methodologies.

In undertaking this evaluation, the researchers specifically targeted the exploration of GPT-3's utility across both sequence- and token-level tasks. The tasks chosen for this exploration include sentiment analysis (SA) with the SST2 dataset, relation extraction (RE) using FewRel, named entity recognition (NER) through the CrossNER dataset, and aspect sentiment triplet extraction (ASTE) on a laptop domain. Three distinct methodologies leveraging GPT-3 for data annotation were proposed: Prompt-Guided Unlabeled Data Annotation (PGDA), Prompt-Guided Training Data Generation (PGDG), and Dictionary-Assisted Training Data Generation (DADG).

Through PGDA, manually crafted prompts are employed to annotate pre-existing unlabeled data, effectively capitalizing on GPT-3’s established prompt-learning capabilities. PGDG explores the capabilities of GPT-3 to autonomously generate datasets suitable for training, while DADG incorporates external knowledge from sources like WikiData to reinforce GPT-3’s dataset generation with domain-specific concepts.

In quantitative terms, the utilization of GPT-3 via these methodologies notably reduced annotation costs across all tasks. For instance, PGDA achieved results on SST2 just slightly below the human-annotated benchmark (87.75 compared to 88.47 in terms of accuracy), while significantly lowering expenditure. In tasks with broader label sets such as FewRel, the generation methods (PGDG and DADG) performed more efficiently than PGDA, highlighting a key insight: generation methods, which don’t demand exhaustive label definitions, are preferable for tasks with wide or ambiguous label spaces.

Data quality produced by GPT-3 approaches is largely contingent upon the nature and size of task label spaces. Tagging-based methods such as PGDA perform optimally with smaller label spaces, whereas generation approaches like PGDG and DADG scale favorably with tasks demanding an elaborate label schema. Such findings are pivotal, suggesting GPT-3’s dual capacity to serve as an annotator through directly prompting annotations and as a generative model to fabricate training datasets.

The paper also examines the interplay of few-shot prompting on GPT-3's annotative proficiency. Contrary to expectation, increased shot environments did not uniformly enhance performance due to GPT-3's propensity to replicate the length and style of provided examples, occasionally veering towards simplistic outputs.

A substantial portion of the analysis juxtaposed GPT-3's annotation efficacy with human annotators, revealing GPT-3’s adeptness in rapidly generating mass-scale annotations albeit with potential sacrifices in minute per-instance quality. Additionally, preliminary tests indicated that ChatGPT might offer a cost-effective alternative to GPT-3 without a significant trade-off in annotation quality, warranting further research.

This investigation yields a pivotal contribution to the discourse surrounding the democratization of AI, illustrating that capable, cost-efficient large-scale data annotation using GPT-3 is achievable. Such developments hold implications for small entities and individual consumers, potentially mitigating resource constraints traditionally associated with high-quality model training. However, challenges linger, primarily concerning bias mitigation and alignment to domain-specific contexts, necessitating further refinement in usage methodologies.

Overall, the paper affirms GPT-3's promising role in data annotation, with pragmatic applications extending across the full spectrum of NLP tasks, subject to continued model and method refinement. The promise of reduced annotation costs and time stands poised to significantly broaden the accessibility and tailoring of AI technologies in diverse settings.

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