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Collections including paper arxiv:2401.04088

about 15 hours ago

PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

Paper • 2402.08714 • Published Feb 13 • 10
Data Engineering for Scaling Language Models to 128K Context

Paper • 2402.10171 • Published Feb 15 • 18
RLVF: Learning from Verbal Feedback without Overgeneralization

Paper • 2402.10893 • Published Feb 16 • 10
Coercing LLMs to do and reveal (almost) anything

Paper • 2402.14020 • Published Feb 21 • 12

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 4
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 3
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

Must-read Papers

Some of the most important and insightful (in my opinion) AI papers of today, with a focus on NLP and LLMs.

ReAct: Synergizing Reasoning and Acting in Language Models

Paper • 2210.03629 • Published Oct 6, 2022 • 13
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 37
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 12
Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 99

Smarter LLMs Research

RARR: Researching and Revising What Language Models Say, Using Language Models

Paper • 2210.08726 • Published Oct 17, 2022 • 1
Hypothesis Search: Inductive Reasoning with Language Models

Paper • 2309.05660 • Published Sep 11, 2023 • 1
In-context Learning and Induction Heads

Paper • 2209.11895 • Published Sep 24, 2022 • 2
ReAct: Synergizing Reasoning and Acting in Language Models

Paper • 2210.03629 • Published Oct 6, 2022 • 13

Must Reads On Language Model

Dive into the world of generative AI with some prominent papers of Language Model, unlocking the secrets of natural language processing.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 12
RoBERTa: A Robustly Optimized BERT Pretraining Approach

Paper • 1907.11692 • Published Jul 26, 2019 • 7
Language Models are Few-Shot Learners

Paper • 2005.14165 • Published May 28, 2020 • 10
OPT: Open Pre-trained Transformer Language Models

Paper • 2205.01068 • Published May 2, 2022 • 1

Mixtral of Experts

Paper • 2401.04088 • Published Jan 8 • 154
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29 • 47
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8 • 68
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models

Paper • 2308.14352 • Published Aug 28, 2023

LLM_architectures

Nemotron-4 15B Technical Report

Paper • 2402.16819 • Published Feb 26 • 41
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

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 91
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 34
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

BlackMamba: Mixture of Experts for State-Space Models

Paper • 2402.01771 • Published Feb 1 • 22
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Paper • 2402.01739 • Published Jan 29 • 26
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29 • 47
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11 • 36

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 • 74
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

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