GPT-3: Language Models as Few-Shot Learners

GPT-3, a 175-billion parameter autoregressive language model, demonstrates remarkable few-shot learning capabilities. Unlike traditional NLP models requiring extensive fine-tuning, GPT-3 adapts to new tasks with minimal examples or just natural language instructions.

This research highlights a shift from task-specific fine-tuning to task-agnostic performance. Humans learn new language tasks from a few examples; GPT-3 aims to replicate this by leveraging its massive scale and in-context learning abilities.

The model was evaluated in three settings: zero-shot (no examples, just instructions), one-shot (one example), and few-shot (10-100 examples). GPT-3 shows significant performance gains with increased model size across all settings.

On many NLP benchmarks, GPT-3's few-shot performance is competitive with, and sometimes surpasses, state-of-the-art fine-tuned models. This includes tasks like translation, question-answering, and cloze tasks, showcasing its versatility.

GPT-3 also excels at tasks requiring on-the-fly reasoning, such as unscrambling words, performing multi-digit arithmetic, and using novel words in sentences after a single definition. This indicates a strong capacity for rapid adaptation.

A notable finding is GPT-3's ability to generate news articles that human evaluators struggle to distinguish from human-written content. This raises important considerations regarding content generation and potential misuse.

Despite its strengths, GPT-3 has limitations. It struggles with certain natural language inference tasks and some reading comprehension datasets, suggesting areas for future improvement in complex reasoning and comparison tasks.

The study also addresses data contamination, a critical concern for models trained on vast web corpora. While some overlap was found, its impact on GPT-3's overall performance was largely minimal, except for a few specific datasets.

The training involved a diverse dataset, including filtered Common Crawl, WebText2, and Wikipedia. Higher-quality datasets were sampled more frequently to optimize for better training data quality, even at the cost of some overfitting.

GPT-3's architecture is similar to GPT-2 but scaled up significantly. Eight different model sizes were trained, from 125 million to 175 billion parameters, demonstrating that performance scales smoothly with model capacity.

The research emphasizes that larger models are more proficient meta-learners, with the gap between zero-shot, one-shot, and few-shot performance often growing with model capacity, indicating improved in-context learning.

The societal implications of such powerful language models are also discussed, including potential for misuse like misinformation and social engineering, as well as issues of bias, fairness, and energy consumption. These require ongoing research and mitigation efforts.