🧠 GPT-3: Language Models are Few-Shot Learners
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🧠 GPT-3: Language Models are Few-Shot Learners
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This source introduces GPT-3, a very large language model with 175 billion parameters, demonstrating its strong ability in few-shot learning across various natural language processing tasks without task-specific fine-tuning. The authors compare GPT-3's performance in zero-shot, one-shot, and few-shot settings to existing state-of-the-art approaches, highlighting its competitiveness and occasional outperformance. Furthermore, the paper explores the model's limitations, potential for misuse in generating human-quality text, and biases reflected from its extensive training data, alongside considerations of energy usage. The research suggests that scaling language models significantly enhances their task-agnostic performance and adaptability.
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