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Collections including paper arxiv:2402.10644

LLM architecture

The Impact of Depth and Width on Transformer Language Model Generalization

Paper • 2310.19956 • Published Oct 30, 2023 • 9
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 • 50

Efficient Memory Management for Large Language Model Serving with PagedAttention

Paper • 2309.06180 • Published Sep 12, 2023 • 25
LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models

Paper • 2308.16137 • Published Aug 30, 2023 • 39
Scaling Transformer to 1M tokens and beyond with RMT

Paper • 2304.11062 • Published Apr 19, 2023 • 2
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Paper • 2309.14509 • Published Sep 25, 2023 • 17

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

Paper • 2402.13249 • Published Feb 20, 2024 • 11
The FinBen: An Holistic Financial Benchmark for Large Language Models

Paper • 2402.12659 • Published Feb 20, 2024 • 17
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20, 2024 • 25
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Paper • 2402.13064 • Published Feb 20, 2024 • 47

Transformers & Attention

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79
Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Paper • 2401.04658 • Published Jan 9, 2024 • 25
KAN: Kolmogorov-Arnold Networks

Paper • 2404.19756 • Published Apr 30, 2024 • 108

Papers - Attention

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Paper • 2305.13245 • Published May 22, 2023 • 5
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23, 2024 • 19
Sequence Parallelism: Long Sequence Training from System Perspective

Paper • 2105.13120 • Published May 26, 2021 • 5

Transformers Can Achieve Length Generalization But Not Robustly

Paper • 2402.09371 • Published Feb 14, 2024 • 13
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79

Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28, 2024 • 18
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1, 2024 • 22
Zoology: Measuring and Improving Recall in Efficient Language Models

Paper • 2312.04927 • Published Dec 8, 2023 • 2

Transformers alternatives

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11, 2024 • 36
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

Paper • 2402.10790 • Published Feb 16, 2024 • 41
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27, 2024 • 605

State Space Models

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

Paper • 2402.01032 • Published Feb 1, 2024 • 22
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Paper • 2402.04248 • Published Feb 6, 2024 • 30
Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Paper • 2402.10644 • Published Feb 16, 2024 • 79
In Search of Needles in a 10M Haystack: Recurrent Memory Finds What LLMs Miss

Paper • 2402.10790 • Published Feb 16, 2024 • 41

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30, 2024 • 17
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Paper • 2401.15688 • Published Jan 28, 2024 • 11
SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Paper • 2401.15024 • Published Jan 26, 2024 • 69
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Paper • 2401.15071 • Published Jan 26, 2024 • 35

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