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Collections including paper arxiv:2401.02038

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

Paper • 2308.08155 • Published Aug 16, 2023 • 2
GAIA: a benchmark for General AI Assistants

Paper • 2311.12983 • Published Nov 21, 2023 • 171
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

Paper • 2303.17580 • Published Mar 30, 2023 • 9
More Agents Is All You Need

Paper • 2402.05120 • Published Feb 3 • 46

Simple and Scalable Strategies to Continually Pre-train Large Language Models

Paper • 2403.08763 • Published Mar 13 • 48
Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 98
Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs

Paper • 2403.20041 • Published Mar 29 • 33
Advancing LLM Reasoning Generalists with Preference Trees

Paper • 2404.02078 • Published Apr 2 • 41

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 90
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 33
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

Specialized Language Models with Cheap Inference from Limited Domain Data

Paper • 2402.01093 • Published Feb 2 • 45
Computing Power and the Governance of Artificial Intelligence

Paper • 2402.08797 • Published Feb 13 • 11
Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 59

Efficient Training

Rethinking Optimization and Architecture for Tiny Language Models

Paper • 2402.02791 • Published Feb 5 • 12
Specialized Language Models with Cheap Inference from Limited Domain Data

Paper • 2402.01093 • Published Feb 2 • 45
Scavenging Hyena: Distilling Transformers into Long Convolution Models

Paper • 2401.17574 • Published Jan 31 • 14
Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 59

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Mixtral of Experts

Paper • 2401.04088 • Published Jan 8 • 152
Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 59

MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19 • 50
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

Paper • 2401.12954 • Published Jan 23 • 28

Large Language Models 2024

Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 59
The Impact of Reasoning Step Length on Large Language Models

Paper • 2401.04925 • Published Jan 10 • 15
Lost in the Middle: How Language Models Use Long Contexts

Paper • 2307.03172 • Published Jul 6, 2023 • 31
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 34

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 26
Tuning Language Models by Proxy

Paper • 2401.08565 • Published Jan 16 • 19
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10 • 62

Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk

Paper • 2401.05033 • Published Jan 10 • 14
Understanding LLMs: A Comprehensive Overview from Training to Inference

Paper • 2401.02038 • Published Jan 4 • 59

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