-
In-context Learning and Induction Heads
Paper • 2209.11895 • Published • 2 -
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Paper • 2404.07129 • Published • 3 -
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Paper • 2403.07809 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2209.11895
-
RARR: Researching and Revising What Language Models Say, Using Language Models
Paper • 2210.08726 • Published • 1 -
Hypothesis Search: Inductive Reasoning with Language Models
Paper • 2309.05660 • Published • 2 -
In-context Learning and Induction Heads
Paper • 2209.11895 • Published • 2 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 16
-
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Paper • 2404.07129 • Published • 3 -
In-context Learning and Induction Heads
Paper • 2209.11895 • Published • 2 -
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Paper • 2403.07809 • Published • 1
-
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Paper • 2404.07129 • Published • 3 -
In-context Learning and Induction Heads
Paper • 2209.11895 • Published • 2 -
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Paper • 2403.07809 • Published • 1
-
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models
Paper • 2311.00871 • Published • 2 -
Can large language models explore in-context?
Paper • 2403.15371 • Published • 32 -
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Paper • 2205.05055 • Published • 2 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 36