ArXiv TLDR

Tom B. Brown

3 papers · Latest:

Natural Language Processing

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.

2005.14165
Machine Learning

Scaling Laws for Neural Language Models

This paper identifies power-law scaling relationships between language model performance and factors like model size, dataset size, and compute, enabling optimal training strategies under fixed compute budgets.

2001.08361
Statistical Machine Learning

Deep reinforcement learning from human preferences

This paper demonstrates that deep reinforcement learning agents can be effectively trained using human preferences as feedback instead of explicit reward functions, enabling complex task learning with minimal human input.

1706.03741

📬 Weekly AI Paper Digest

Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.