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Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 104 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 114 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60 -
Do language models plan ahead for future tokens?
Paper • 2404.00859 • Published • 2
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Collections including paper arxiv:2402.03620
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Communicative Agents for Software Development
Paper • 2307.07924 • Published • 4 -
Self-Refine: Iterative Refinement with Self-Feedback
Paper • 2303.17651 • Published • 2 -
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Paper • 2312.10003 • Published • 37 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 15
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PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 57 -
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Paper • 2403.13447 • Published • 18 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 114 -
RAFT: Adapting Language Model to Domain Specific RAG
Paper • 2403.10131 • Published • 67
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Unified Functional Hashing in Automatic Machine Learning
Paper • 2302.05433 • Published • 2 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 114 -
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells
Paper • 2401.07278 • Published • 2
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ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Paper • 2403.03853 • Published • 61 -
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Paper • 2402.09025 • Published • 6 -
Shortened LLaMA: A Simple Depth Pruning for Large Language Models
Paper • 2402.02834 • Published • 14 -
Algorithmic progress in language models
Paper • 2403.05812 • Published • 18
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Measuring the Effects of Data Parallelism on Neural Network Training
Paper • 1811.03600 • Published • 2 -
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Paper • 1804.04235 • Published • 2 -
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Paper • 1905.11946 • Published • 3 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 62
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AtP*: An efficient and scalable method for localizing LLM behaviour to components
Paper • 2403.00745 • Published • 12 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 605 -
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT
Paper • 2402.16840 • Published • 23 -
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Paper • 2402.13753 • Published • 114