What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
Paper
•
2312.15685
•
Published
•
16
AugmentedLearning
-
-
mistralai/Mixtral-8x7B-Instruct-v0.1
47B • Updated • 535k • 4.58k -
microsoft/phi-2
Text Generation • 3B • Updated • 704k • 3.41k -
TinyLlama/TinyLlama-1.1B-Chat-v1.0
Text Generation • 1B • Updated • 4.29M • 1.44k -
Are Emergent Abilities in Large Language Models just In-Context Learning?
Paper • 2309.01809 • Published • 3 -
Commonsense Knowledge Transfer for Pre-trained Language Models
Paper • 2306.02388 • Published • 1 -
Schema-learning and rebinding as mechanisms of in-context learning and emergence
Paper • 2307.01201 • Published • 2 -
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Paper • 2305.01610 • Published • 2 -
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
Paper • 2308.12066 • Published • 4 -
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Paper • 2308.06093 • Published • 2 -
Multi-Head Adapter Routing for Cross-Task Generalization
Paper • 2211.03831 • Published • 2 -
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception
Paper • 2305.06324 • Published • 1 -
Multimodal Foundation Models: From Specialists to General-Purpose Assistants
Paper • 2309.10020 • Published • 41 -
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Paper • 2306.05425 • Published • 11 -
Evaluation and Mitigation of Agnosia in Multimodal Large Language Models
Paper • 2309.04041 • Published • 1 -
From Sparse to Soft Mixtures of Experts
Paper • 2308.00951 • Published • 21