Collections
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Collections including paper arxiv:2203.02155
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Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 23 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
Hierarchical Reasoning Model
Paper • 2506.21734 • Published • 50 -
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 513 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24
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EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning
Paper • 2509.22576 • Published • 137 -
AgentBench: Evaluating LLMs as Agents
Paper • 2308.03688 • Published • 26 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 22 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 64
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Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
Evaluating Large Language Models Trained on Code
Paper • 2107.03374 • Published • 10 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24 -
GPT-4 Technical Report
Paper • 2303.08774 • Published • 7
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Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation
Paper • 2510.23581 • Published • 42
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Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7 -
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Hierarchical Reasoning Model
Paper • 2506.21734 • Published • 50
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DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Paper • 2503.14476 • Published • 146 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251 -
The Llama 3 Herd of Models
Paper • 2407.21783 • Published • 118
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DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 3 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20
-
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
Evaluating Large Language Models Trained on Code
Paper • 2107.03374 • Published • 10 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24 -
GPT-4 Technical Report
Paper • 2303.08774 • Published • 7
-
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 23 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251
-
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation
Paper • 2510.23581 • Published • 42
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
Hierarchical Reasoning Model
Paper • 2506.21734 • Published • 50 -
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 513 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24
-
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 7 -
Attention Is All You Need
Paper • 1706.03762 • Published • 120 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Hierarchical Reasoning Model
Paper • 2506.21734 • Published • 50
-
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Paper • 2503.14476 • Published • 146 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 24 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251 -
The Llama 3 Herd of Models
Paper • 2407.21783 • Published • 118
-
EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning
Paper • 2509.22576 • Published • 137 -
AgentBench: Evaluating LLMs as Agents
Paper • 2308.03688 • Published • 26 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 22 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 64
-
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 3 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20