Leveraging GPT-3 for Cost-effective Data Labeling in NLP Tasks
In the paper "Want To Reduce Labeling Cost? GPT-3 Can Help," the authors investigate a novel approach to reducing data labeling costs in NLP by utilizing the GPT-3 LLM. The primary objective is to explore GPT-3 as a low-cost labeling tool to enhance the training of downstream models, thereby achieving comparable performance with human-labeled data but at a fraction of the cost. This paper examines the efficacy of employing GPT-3-generated labels in combination with human labels across various NLP tasks, including both natural language understanding (NLU) and natural language generation (NLG).
The authors highlight the financial burden associated with human annotation and the importance of discovering cost-efficient alternatives. GPT-3's ability to improve performance in few-shot learning is leveraged to annotate data for training smaller models that require less computational resources. This significantly reduces the need for extensive human labeling. The empirical analysis demonstrates that using GPT-3 labels results in a cost reduction ranging from 50% to 96% compared to human labeling, while still achieving equivalent model performance on diverse NLP tasks. For instance, in the Stanford Sentiment Treebank (SST-2), using GPT-3 reduced labeling costs dramatically by 96%.
Furthermore, the paper proposes a dual supervision framework that combines pseudo-labels generated from GPT-3 with human labels to enhance model performance under constrained labeling budgets. This hybrid approach optimizes the allocation of labeling tasks between GPT-3 and human annotators to maximize both cost savings and labeling accuracy.
A key contribution of this work is the introduction of an active labeling strategy. This method identifies instances labeled by GPT-3 with low confidence scores and re-annotates them using human labelers, thereby improving overall labeling quality. The strategy demonstrates clear performance improvements over using a single source of labeler, emphasizing the efficacy of incorporating confidence-based human interventions.
From a theoretical perspective, the authors provide a framework to justify why models trained with GPT-3-generated labels might outperform GPT-3 itself in few-shot settings. Under certain consistency assumptions and expansion properties, they demonstrate that the error rate of a model trained with GPT-3 labels can be theoretically lower than the error rate of GPT-3 in few-shot deployments.
The paper conducts experiments across nine NLP tasks, encompassing tasks like sentiment analysis, text entailment, summarization, and question generation, to validate the proposed cost-effective labeling strategies. The findings consistently confirm the advantages of GPT-3 labeling in reducing costs and enhancing model performance within budget constraints.
While this paper effectively demonstrates the practical benefits of GPT-3 as a cost-efficient labeler, it acknowledges limitations in high-stakes scenarios where label accuracy is critical. Future research could extend the proposed methods to data augmentation processes that generate both instances and labels, thereby further enriching the training data without incurring additional costs.
In conclusion, this research underscores the potential of GPT-3 as a powerful tool for reducing data labeling costs in NLP applications. By strategically integrating GPT-3's capabilities with human annotation, the proposed methodologies present a feasible approach to pragmatically balance cost and performance, promising significant operational efficiencies in various NLP domains.