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Collections including paper arxiv:2402.07871

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Chain-of-Verification Reduces Hallucination in Large Language Models

Paper • 2309.11495 • Published Sep 20, 2023 • 37
Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 77
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

Paper • 2309.09400 • Published Sep 17, 2023 • 85
Language Modeling Is Compression

Paper • 2309.10668 • Published Sep 19, 2023 • 83

Mixtral of Experts

Paper • 2401.04088 • Published Jan 8, 2024 • 157
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 51
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8, 2024 • 70
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models

Paper • 2308.14352 • Published Aug 28, 2023

Mixture of Experts

BlackMamba: Mixture of Experts for State-Space Models

Paper • 2402.01771 • Published Feb 1, 2024 • 24
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29, 2024 • 27
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29, 2024 • 51
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 48

Scaling Laws for Downstream Task Performance of Large Language Models

Paper • 2402.04177 • Published Feb 6, 2024 • 18
A Tale of Tails: Model Collapse as a Change of Scaling Laws

Paper • 2402.07043 • Published Feb 10, 2024 • 14
Scaling Laws for Fine-Grained Mixture of Experts

Paper • 2402.07871 • Published Feb 12, 2024 • 12
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Paper • 2402.17193 • Published Feb 27, 2024 • 24

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 19
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 80
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1, 2024 • 24
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29, 2024 • 53
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 19
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23, 2024 • 19
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21, 2024 • 6

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 48
Mixtral of Experts

Paper • 2401.04088 • Published Jan 8, 2024 • 157
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4, 2024 • 49
LLM Augmented LLMs: Expanding Capabilities through Composition

Paper • 2401.02412 • Published Jan 4, 2024 • 37

YAYI 2: Multilingual Open-Source Large Language Models

Paper • 2312.14862 • Published Dec 22, 2023 • 14
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Paper • 2312.15166 • Published Dec 23, 2023 • 57
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10, 2024 • 69
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11, 2024 • 48

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