Recent research has focused on understanding in-context learning in large language models (LLMs) like GPT-3+. These models have shown significant performance improvements by predicting the next word in a sequence, using larger training datasets and increased model capacity. In-context learning allows the model to learn tasks by conditioning a series of examples without explicit training, but its working mechanism is not fully understood.
Researchers from UCLA explored three methods of in-context learning in LLMs through binary classification tasks (BCTs) under varying conditions. The study aimed to link in-context learning with gradient descent, understand its practical implications in LLMs, and explore learning to learn in-context using MetaICL. The experiments revealed that finetuning LLMs on in-context examples did not result in smoother decision boundaries, even with different factors considered.
The decision boundaries of LLMs were explored for classification tasks using various datasets and LLMs with different parameters. The results showed that the decision boundaries remained non-smooth even after finetuning, prompting further investigation into factors affecting decision boundary smoothness.
Overall, the research provides insights into the mechanics of in-context learning in LLMs and suggests pathways for future research and optimization. The study proposes a novel method to understand in-context learning by examining decision boundaries in BCTs and highlights the need for further exploration into improving decision boundary smoothness in LLMs.
Source link
Source link: https://www.marktechpost.com/2024/06/26/a-new-machine-learning-research-from-ucla-uncovers-unexpected-irregularities-and-non-smoothness-in-llms-in-context-decision-boundaries/?amp