How many Labelled Examples do you need for a BERT-sized Model to Beat GPT-4 on Predictive Tasks?

2023-10-25 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

An analysis comparing BERT-sized models with GPT-4 on predictive NLP tasks indicates that models under 1 billion parameters can surpass GPT-4's accuracy, particularly when using supervised approaches. This finding challenges assumptions about the universal superiority of large language models, highlighting that classic predictive NLP problems are often better handled by smaller, fine-tuned models. The core question addressed is the number of labeled examples required for a BERT-sized model to outperform GPT-4, revealing that in-context learning struggles with many problem shapes where traditional supervised methods excel.

Key takeaway

For Machine Learning Engineers evaluating models for predictive NLP tasks, you should reconsider the assumption that larger models like GPT-4 are always superior. Your focus should shift towards fine-tuning BERT-sized models, especially when sufficient labeled data is available, as they often achieve higher accuracy and efficiency. Prioritize supervised approaches for classic predictive problems where in-context learning struggles, optimizing resource allocation and model performance.

Key insights

BERT-sized models often beat GPT-4 on predictive NLP tasks with sufficient labeled data.

Principles

• Smaller models excel in classic predictive NLP.
• In-context learning has problem shape limitations.
• Supervised learning remains highly competitive.

In practice

• Prioritize supervised BERT for predictive NLP.
• Evaluate in-context learning for problem fit.
• Consider model size for task-specific accuracy.

Topics

• BERT-sized Models
• GPT-4
• Predictive NLP
• In-context Learning
• Supervised Learning
• Model Performance

Best for: AI Engineer, AI Architect, Research Scientist, Machine Learning Engineer, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.