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Is GPT-3 a Good Data Annotator?

Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Shafiq Joty, Boyang Li, Lidong Bing

arXiv:2212.10450v2cs.CL

TL;DR

The paper asks whether GPT-3 can provide effective NLP training-data annotation despite the cost of obtaining high-quality labels. It evaluates three GPT-3-based approaches across varied tasks and finds that GPT-3 can offer relatively low-cost annotation, with performance often comparable to or better than human-annotated training under limited budgets, though quality remains improvable.

  • Problem

    High-quality annotations are essential for supervised NLP, but annotation costs can prevent individuals and small organizations from producing sufficient training data.

  • Method

    The paper evaluates 3 GPT-3-based annotation approaches across sequence- and token-level NLP tasks, measuring performance, time, and cost-effectiveness.

  • Results

    GPT-3 can annotate different tasks at relatively lower cost, while models trained on its annotations are often comparable to or better than models trained on human-annotated data under limited budgets.

  • Takeaways & Limitations

    Direct annotation suits small label spaces, generation-based methods suit large label spaces, and generation-based approaches tend to be more cost-effective.

  • Takeaways & Limitations

    Financial constraints limited large-scale experimentation, so the findings may not fully represent larger datasets or populations; GPT-3 is also difficult to interpret as a black box.

Abstract

from arXiv · show

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 language model 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.

1 Introduction

The paper examines GPT-3 as an NLP data annotator because high-quality labeled data is costly and difficult for resource-constrained organizations to obtain. It evaluates three GPT-3-based approaches across sequence- and token-level tasks, finding task-dependent suitability and cost advantages for generation-based methods.

  • Motivation: Accurate supervised-learning annotations require substantial labor, hiring, training, management, tooling, and infrastructure costs.These costs can prevent individuals and small organizations from creating sufficient training data.
  • Motivation: GPT-3 can perform diverse NLP tasks but is difficult to deploy directly because of its size, computational requirements, and limited local deployability.Smaller models such as BERTBASE are often more feasible for production environments.
  • Contributions: The study evaluates 3 GPT-3-based annotation approaches on both sequence- and token-level NLP tasks, measuring performance, time, and cost-effectiveness.The paper frames this analysis as an investigation of GPT-3's feasibility for complex NLP data annotation.
  • Contributions: Directly annotating unlabeled data is suitable for tasks with small label spaces, whereas generation-based methods are more suitable for tasks with large label spaces.This is presented as a cross-task finding from the paper's experiments.
  • Contributions: Generation-based approaches tend to be more cost-effective than directly annotating unlabeled data.The paper positions this as a practical advantage of generating training data rather than only assigning labels to existing unlabeled examples.

2 Related Work

Related work situates the study within large-language-model prompting, GPT-3-based annotation, and limited-label NLP. The paper extends prior GPT-3 annotation work to broader task settings and examines zero-shot training-data generation without seed examples.

  • Large Language Models: Large language models use self-supervised pretraining and few-shot in-context learning to perform numerous NLP tasks.This provides the broader modeling context for using GPT-3 as an annotator.
  • Large Language Models: Prior GPT-3 annotation research mainly addressed generation and sequence-classification tasks.The paper contrasts its scope with this narrower earlier focus.
  • Large Language Models: This work expands evaluation to a wider range of settings covering both sequence- and token-level tasks.The broader coverage is identified as a distinction from prior work.
  • Large Language Models: The paper analyzes zero-shot training-data generation, which does not require seed examples, alongside worker-and-AI dataset creation using a few seed examples.This places the paper's contribution relative to collaborative dataset-construction approaches.
  • Prompt-Learning: Prompt-learning connects pretraining and model tuning by conditioning language models on task-specific prompts and has proven effective in low-data regimes.Related studies also report that prompt optimization can approach full fine-tuning performance for large pretrained models.

3 Methodology

The methodology uses in-context prompts to obtain labels or generate labeled examples, optionally incorporating Wikidata as external domain knowledge. The resulting GPT-3-produced data trains a local model evaluated on human-labeled test data.

  • Overview: The study compares 3 approaches: prompt-guided unlabeled data annotation, prompt-guided training data generation, and dictionary-assisted training data generation.All three approaches condition GPT-3 on input-output pairs in an in-context learning setup.
  • Overview: In-context learning conditions GPT-3 on a list of input-output pairs, with the query input as x_i and generated text as y_i.The framework can become instruction-tuning under zero-shot settings when no input-output pairs are provided.
  • PGDA: PGDA prompts GPT-3 with task-specific few-shot examples and unlabeled data, then uses its predicted labels to create training data for a local model.The local model is evaluated by predicting human-labeled test data.
  • PGDG: PGDG prompts GPT-3 to autonomously generate labeled data for a specified task, which is then used to train a local model.For relation extraction, generation can begin by producing entity pairs associated with a specified relation.
  • DADG: DADG queries Wikidata for domain-specific entity pairs and prompts GPT-3 to generate sentences expressing the specified relation.This incorporates external knowledge without fine-tuning and can help when a domain is absent from GPT-3's pretraining corpus.

4 Experiments

The experiments evaluate three GPT-3 annotation approaches across sequence- and token-level NLP tasks under production-oriented settings. Results show that the strongest approach depends on task structure, label-space size, and access to domain entities.

  • Experimental setup: The study evaluates GPT-3 annotation on sentiment analysis, relation extraction, named entity recognition, and aspect sentiment triplet extraction.Experiments use SST2, FewRel, the AI-domain CrossNER split, and the laptop-domain ASTE split.
  • Experimental setup: The experiments compare direct annotation, generation-based annotation, and Wikidata-assisted generation, with resulting data reformatted to fine-tune BERTBASE.The approaches use few-shot examples and, where applicable, entities retrieved from Wikidata.
  • Sequence-level tasks: 87.75 accuracy: PGDA nearly matches human-labeled SST2 data while using substantially less time and cost, including approximately 10% of human-labeling cost for 6,000 examples.PGDA is evaluated in a 10-shot setting; its 3,000-example result is 0.72 below human-labeled data.
  • Sequence-level tasks: 33-point and 23-point F1 scores: under comparable costs, PGDG and DADG outperform 704 human-labeled FewRel examples, while PGDG consistently exceeds DADG.With only 1-shot data, PGDG and DADG reach around 44 and 40 F1 points respectively; 5-shot performance improves further.
  • Token-level tasks: PGDA performs worst and costs most on CrossNER, whereas PGDG matches the 100-example human-labeled baseline more cheaply and DADG performs better by using unique in-domain entities.PGDA can produce wrong entity types and inaccurate entity boundaries, especially as the label space grows.
  • Token-level tasks: PGDA performs best on ASTE, while PGDG tends to generate data with explicit sentiment and DADG is disadvantaged because entities are not key factors for the task.Performance varies across prompts, and the reported comparison parallels the SST2 findings about entity relevance.

5 Further Analysis

The analysis examines how label-space size, data distributions, shot count, and model choice affect GPT-3-based annotation. Tagging is favored for small, clear label spaces, while generation is better suited to larger or ambiguous spaces and can be more cost-effective.

  • 5.1 Impact of Label Space: Tagging-based PGDA suits small, clearly defined label spaces, whereas generation-based PGDG and DADG suit larger or ambiguous label spaces.The paper gives sentiment analysis and ASTE as small-label examples, and CrossNER and FewRel as larger-label examples.
  • 5.2 Comparison with Human Annotators: Generation-based approaches reduce label errors and avoid span-boundary detection challenges while using shorter prompts and generating multiple data points per prompt.These properties contribute to their reported cost-effectiveness relative to tagging-based annotation.
  • 5.2 Comparison with Human Annotators: GPT-3 can rapidly generate domain-specific, structured, and unstructured data, but human annotations generally retain higher per-instance quality when training data is limited.The comparison favors machines for speed and scale, especially for labeled-data generation without unlabeled data.
  • 5.3 Impact of Number of Shots: More shots do not consistently improve annotation: PGDA benefits on SST2, while PGDG and DADG may imitate short, low-information examples.For FewRel, five-shot context can produce more implicit relations than zero- or one-shot context because its examples contain richer relational information.
  • 5.4 Preliminary Comparison between GPT-3 and ChatGPT: ChatGPT performs on par with GPT-3 on SST2 and is described as more cost-efficient in the preliminary comparison.The passage also cites external findings that ChatGPT surpassed crowd-workers on several tweet-annotation tasks.
  • 5 Further Analysis: Zero-shot and few-shot GPT-3, ChatGPT, and GPT-4 experiments indicate that these models can annotate multilingual training data with minimal or no prior exposure to target languages.This observation is reported for the multilingual setting examined in Appendix A.14.

6 Conclusions

The paper evaluates GPT-3 as an annotator across NLP tasks using three approaches and finds potential for lower-cost annotation. Performance is often comparable to or better than human-annotated training under limited budgets, although data quality still has room to improve.

  • 6 Conclusions: The study investigates GPT-3 as a data annotator for varied NLP tasks using three main approaches.The experiments cover both sequence- and token-level tasks.
  • 6 Conclusions: GPT-3 annotation can support different tasks at relatively lower cost, especially for individuals or organizations with limited budgets.The conclusion frames this potential in relation to broader access to model-training data.
  • 6 Conclusions: With limited budgets, models trained on GPT-3-annotated data are often comparable to or better than models trained on human-annotated data.The conclusion simultaneously states that GPT-3-annotated data quality still has room for improvement compared with human annotations.

7 Limitations

The study’s experiments were constrained by financial limitations, and its findings may not represent larger datasets or populations. GPT-3 also presents interpretability challenges as a black-box system.

  • 7 Limitations: Financial constraints prevented large-scale experimentation, so the findings may not fully represent larger datasets or populations.The limitation directly concerns the scale and representativeness of the experimental evidence.
  • 7 Limitations: Using GPT-3 creates interpretability challenges because the model operates as a black-box system.The authors propose larger experiments and comparisons with other language models as future work.

Ethics Consideration

The ethics discussion identifies a risk that GPT-3 may reinforce biases and stereotypes present in its pretraining data. It recommends diverse training data and ongoing monitoring of generated outputs.

  • Ethics Consideration: GPT-3 may reinforce existing biases in the datasets it annotates because its pretraining data may contain bias and stereotypes.The paper connects this risk to the model’s pretraining on vast amounts of unlabeled data.
  • Ethics Consideration: The paper recommends diverse, representative training data and consistent output monitoring to identify and rectify possible biases.These measures are presented as responses to the bias-reinforcement concern.

A.1 PGDA for SST2

Figure 8 illustrates prompt-guided annotation of unlabeled SST2 data, showing how GPT-3 is prompted to assign labels.

  • Prompt-guided annotation applies GPT-3 to unlabeled SST2 examples.The figure presents an example of the prompting setup for annotation.
  • The SST2 annotation process uses a prompt to obtain labels for unlabeled data.
  • Figure 8 provides a concrete example of GPT-3-based annotation for SST2.

A.2 PGDG for SST2

Figure 9 illustrates prompt-guided data generation for SST2, using GPT-3 to produce training examples.

  • Prompt-guided generation uses GPT-3 to create SST2 training data.The figure provides an example of the generation procedure.
  • The SST2 generation process is controlled through a prompt.
  • Figure 9 shows a concrete example of GPT-3 generating data for SST2.

A.3 DADG for SST2

The appendix presents prompt examples and case studies for GPT-3-based annotation and generation across NLP tasks. It also reports domain-specific knowledge and multilingual annotation capabilities, while discussing annotation support for individuals and SMEs.

  • Prompt examples: The paper presents prompt examples for annotation and generation across SST2, CrossNER, ASTE, and FewRel.The figures cover unlabeled-data annotation, data generation, entity typing, relation identification, and triplet-conditioned sentence generation.
  • Generated data: GPT-3-generated SST2 sentences contain more explicit sentiment expressions than gold training data.
  • Generated data: The paper uses zero-shot, 1-shot, and 5-shot settings to illustrate generated sentences for SST2 and FewRel.
  • Motivation and discussion: The authors aim to assist individuals and SMEs with data annotation, which is costly because of labeling labor, hiring, training, management, tools, and infrastructure.
  • Multilingual annotation: Figures 30–32 illustrate GPT-3's domain-specific knowledge and the use of GPT-3, ChatGPT, and GPT-4 for non-English data annotation.
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