A New Machine Learning Research from UCLA Uncovers Unexpected Irregularities and Non-Smoothness in LLMs’ In-Context Decision Boundaries

Recent language models like GPT-3+ have shown remarkable performance improvements by simply predicting the next word in a sequence, using larger training datasets and increased model capacity. A key feature of these transformer-based models is in-context learning, which allows the model to learn tasks by conditioning a series of examples without explicit training. However, the […]

The post A New Machine Learning Research from UCLA Uncovers Unexpected Irregularities and Non-Smoothness in LLMs’ In-Context Decision Boundaries appeared first on MarkTechPost.

ai paper summary ai shorts applications artificial intelligence capacity context context learning datasets decision editors pick feature gpt gpt-3 improvements in-context learning key language language models learn llms machine machine learning next performance research staff tech news technology training training datasets transformer transformer-based models ucla word