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Collections including paper arxiv:2402.01032

new architecture

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 48
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 51
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
BlackMamba: Mixture of Experts for State-Space Models

Paper • 2402.01771 • Published Feb 1 • 23

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 78
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

Interesting Papers

Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22

State Space Models

Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6 • 30
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16 • 78
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

Paper • 2402.10790 • Published Feb 16 • 40

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

Paper • 2402.19427 • Published Feb 29 • 52
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

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

Paper • 2402.14180 • Published Feb 21 • 6

ParadigmShift-Inquiry

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 36
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 17
LLM Augmented LLMs: Expanding Capabilities through Composition

Paper • 2401.02412 • Published Jan 4 • 36
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11 • 43
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 21

Large-Scale Automatic Audiobook Creation

Paper • 2309.03926 • Published Sep 7, 2023 • 53
Agents: An Open-source Framework for Autonomous Language Agents

Paper • 2309.07870 • Published Sep 14, 2023 • 42
PDFTriage: Question Answering over Long, Structured Documents

Paper • 2309.08872 • Published Sep 16, 2023 • 53
StarCoder: may the source be with you!

Paper • 2305.06161 • Published May 9, 2023 • 30

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