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PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Paper • 2310.17752 • Published • 11 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 28 -
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Paper • 2311.06243 • Published • 17 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28
Collections
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Collections including paper arxiv:2311.08401
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Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 2 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47
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Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 53 -
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion
Paper • 2310.08579 • Published • 14 -
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
Paper • 2310.12921 • Published • 19 -
De-Diffusion Makes Text a Strong Cross-Modal Interface
Paper • 2311.00618 • Published • 21
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 118 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14
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Large Language Models as Optimizers
Paper • 2309.03409 • Published • 75 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 32 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 86 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 25