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Collections including paper arxiv:2404.09173

LLM_architectures

Nemotron-4 15B Technical Report

Paper • 2402.16819 • Published Feb 26 • 40
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 10
Reformer: The Efficient Transformer

Paper • 2001.04451 • Published Jan 13, 2020

Foundation AI Papers

Curated List of Must-Reads on LLM reasoning at Temus AI team

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Paper • 2310.04406 • Published Oct 6, 2023 • 8
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 91
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14 • 6
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 104

OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework

Paper • 2404.14619 • Published Apr 22 • 122
Multi-Head Mixture-of-Experts

Paper • 2404.15045 • Published Apr 23 • 55
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22 • 239
Learn Your Reference Model for Real Good Alignment

Paper • 2404.09656 • Published Apr 15 • 80

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 62
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10 • 95

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 570
BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 94
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2 • 102
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42

TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42
Ring Attention with Blockwise Transformers for Near-Infinite Context

Paper • 2310.01889 • Published Oct 3, 2023 • 8
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Paper • 2404.08801 • Published Apr 12 • 62

TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42

TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42

Compression Represents Intelligence Linearly

Paper • 2404.09937 • Published Apr 15 • 27
TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42

TransformerFAM: Feedback attention is working memory

Paper • 2404.09173 • Published Apr 14 • 42

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