Language models are few-shot learners

Summary

This paper introduces GPT-3, a large language model with 175 billion parameters, demonstrating that language models can perform a variety of natural language tasks with few-shot learning. The study explores the ability of GPT-3 to perform tasks such as translation, question answering, and text generation without task-specific fine-tuning. The paper evaluates GPT-3 on a wide range of tasks, including closed-book question answering, common sense reasoning, reading comprehension, and code generation, using few-shot, one-shot, and zero-shot learning paradigms. Results demonstrate that GPT-3 achieves state-of-the-art performance on many NLP benchmarks, often surpassing prior models even with limited examples. The paper analyzes the emergent abilities of large language models and their potential for generalization and adaptation to new tasks with minimal training data.

Key Takeaways

1. GPT-3 achieves impressive performance on various NLP tasks with few-shot learning, requiring only a few examples to adapt to new tasks.
2. The paper showcases the scaling of language models, highlighting the correlation between model size and performance.
3. GPT-3's ability to perform diverse tasks without task-specific training underscores the potential of large language models for general-purpose language understanding and generation.
4. The study introduces and demonstrates the viability of the 'few-shot' learning paradigm for language models, significantly reducing the reliance on massive, task-specific datasets.