This paper introduces GPT-3, a 175-billion-parameter autoregressive language model that achieves impressive zero-shot, one-shot, and few-shot performance across diverse NLP tasks without task-specific fine-tuning. Its scale allows it to generalize from natural language prompts, rivaling or surpassing prior state-of-the-art models that require fine-tuning. The paper’s impact is profound: it demonstrated the power of scaling laws, reshaped research on few-shot learning, and sparked widespread adoption of large-scale language models, influencing advancements in AI applications, ethical debates, and commercial deployments globally.
