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Collections including paper arxiv:2311.10770

LLM architecture

The Impact of Depth and Width on Transformer Language Model Generalization

Paper • 2310.19956 • Published Oct 30, 2023 • 10
Retentive Network: A Successor to Transformer for Large Language Models

Paper • 2307.08621 • Published Jul 17, 2023 • 170
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 15
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 49

Training & Architectures

Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 49
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Paper • 2307.08691 • Published Jul 17, 2023 • 8
Mixtral of Experts

Paper • 2401.04088 • Published Jan 8, 2024 • 157
Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 46

Scaling MLPs: A Tale of Inductive Bias

Paper • 2306.13575 • Published Jun 23, 2023 • 14
Trap of Feature Diversity in the Learning of MLPs

Paper • 2112.00980 • Published Dec 2, 2021 • 1
Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics

Paper • 2301.05816 • Published Jan 14, 2023 • 1
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

Paper • 2108.04384 • Published Aug 9, 2021 • 1

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 27
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 96
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Paper • 2310.11511 • Published Oct 17, 2023 • 76
In-Context Learning Creates Task Vectors

Paper • 2310.15916 • Published Oct 24, 2023 • 43
Matryoshka Diffusion Models

Paper • 2310.15111 • Published Oct 23, 2023 • 42

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18, 2024 • 146
Exponentially Faster Language Modelling

Paper • 2311.10770 • Published Nov 15, 2023 • 118
Fine-tuning Language Models for Factuality

Paper • 2311.08401 • Published Nov 14, 2023 • 29
NEFTune: Noisy Embeddings Improve Instruction Finetuning

Paper • 2310.05914 • Published Oct 9, 2023 • 14

FreeU: Free Lunch in Diffusion U-Net

Paper • 2309.11497 • Published Sep 20, 2023 • 65
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Paper • 2309.08532 • Published Sep 15, 2023 • 53
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Paper • 2309.12307 • Published Sep 21, 2023 • 88
Mistral 7B

Paper • 2310.06825 • Published Oct 10, 2023 • 46

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

Paper • 2309.09400 • Published Sep 17, 2023 • 85
Contrastive Decoding Improves Reasoning in Large Language Models

Paper • 2309.09117 • Published Sep 17, 2023 • 38
FreeU: Free Lunch in Diffusion U-Net

Paper • 2309.11497 • Published Sep 20, 2023 • 65
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models

Paper • 2309.11674 • Published Sep 20, 2023 • 31

DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Paper • 2309.03883 • Published Sep 7, 2023 • 35
LoRA: Low-Rank Adaptation of Large Language Models

Paper • 2106.09685 • Published Jun 17, 2021 • 32
Agents: An Open-source Framework for Autonomous Language Agents

Paper • 2309.07870 • Published Sep 14, 2023 • 42
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Paper • 2309.00267 • Published Sep 1, 2023 • 47

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