Jack Clark
4 papers · Latest:
Discovering Language Model Behaviors with Model-Written Evaluations
This paper introduces a method to automatically generate high-quality evaluations using language models themselves, revealing new and unexpected behaviors as models scale.
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
This paper demonstrates that reinforcement learning from human feedback (RLHF) can effectively fine-tune language models to be both helpful and harmless, improving performance across NLP tasks while maintaining specialized skills.
Learning Transferable Visual Models From Natural Language Supervision
This paper presents CLIP, a model that learns versatile visual representations by training on 400 million image-text pairs, enabling zero-shot transfer to diverse vision tasks without task-specific training.
Language Models are Few-Shot Learners
GPT-3, a 175 billion parameter language model, demonstrates strong few-shot learning abilities across diverse NLP tasks without task-specific fine-tuning.
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