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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 153 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2407.21783
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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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High-Resolution Image Synthesis with Latent Diffusion Models
Paper • 2112.10752 • Published • 17 -
Adding Conditional Control to Text-to-Image Diffusion Models
Paper • 2302.05543 • Published • 58 -
Proximal Policy Optimization Algorithms
Paper • 1707.06347 • Published • 11 -
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 -
Large Language Models Are Human-Level Prompt Engineers
Paper • 2211.01910 • Published • 1 -
Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 44 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3
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VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Paper • 2602.10693 • Published • 220 -
Reinforced Attention Learning
Paper • 2602.04884 • Published • 29 -
Learning to Reason in 13 Parameters
Paper • 2602.04118 • Published • 6 -
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
Paper • 2405.17604 • Published • 3
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Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Paper • 2511.22699 • Published • 245 -
A Survey on Diffusion Language Models
Paper • 2508.10875 • Published • 34 -
Scalable Diffusion Models with Transformers
Paper • 2212.09748 • Published • 17 -
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Paper • 2403.03206 • Published • 71
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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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Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 153 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Paper • 2602.10693 • Published • 220 -
Reinforced Attention Learning
Paper • 2602.04884 • Published • 29 -
Learning to Reason in 13 Parameters
Paper • 2602.04118 • Published • 6 -
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
Paper • 2405.17604 • Published • 3
-
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
-
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Paper • 2511.22699 • Published • 245 -
A Survey on Diffusion Language Models
Paper • 2508.10875 • Published • 34 -
Scalable Diffusion Models with Transformers
Paper • 2212.09748 • Published • 17 -
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Paper • 2403.03206 • Published • 71
-
High-Resolution Image Synthesis with Latent Diffusion Models
Paper • 2112.10752 • Published • 17 -
Adding Conditional Control to Text-to-Image Diffusion Models
Paper • 2302.05543 • Published • 58 -
Proximal Policy Optimization Algorithms
Paper • 1707.06347 • Published • 11 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 64
-
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
-
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
Large Language Models Are Human-Level Prompt Engineers
Paper • 2211.01910 • Published • 1 -
Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 44 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3
-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2