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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
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:2401.16380
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Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 18 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 44 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 77
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AgentInstruct: Toward Generative Teaching with Agentic Flows
Paper • 2407.03502 • Published • 50 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Paper • 2406.08464 • Published • 71 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 257 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31
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Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 50 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 31 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 13 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50
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Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 88 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 29
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 31 -
Tuning Language Models by Proxy
Paper • 2401.08565 • Published • 22 -
TrustLLM: Trustworthiness in Large Language Models
Paper • 2401.05561 • Published • 69
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SEA-LION: Southeast Asian Languages in One Network
Paper • 2504.05747 • Published -
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
Paper • 2408.02237 • Published -
A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs
Paper • 2406.17377 • Published -
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Paper • 2306.11372 • Published
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Textbooks Are All You Need
Paper • 2306.11644 • Published • 146 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 88 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 36 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 104
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 31 -
Tuning Language Models by Proxy
Paper • 2401.08565 • Published • 22 -
TrustLLM: Trustworthiness in Large Language Models
Paper • 2401.05561 • Published • 69
-
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 18 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 44 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 77
-
SEA-LION: Southeast Asian Languages in One Network
Paper • 2504.05747 • Published -
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
Paper • 2408.02237 • Published -
A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs
Paper • 2406.17377 • Published -
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Paper • 2306.11372 • Published
-
AgentInstruct: Toward Generative Teaching with Agentic Flows
Paper • 2407.03502 • Published • 50 -
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Paper • 2406.08464 • Published • 71 -
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Paper • 2404.14219 • Published • 257 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31
-
Textbooks Are All You Need
Paper • 2306.11644 • Published • 146 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 88 -
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 36 -
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Paper • 2406.20094 • Published • 104
-
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling
Paper • 2401.16380 • Published • 50 -
Best Practices and Lessons Learned on Synthetic Data for Language Models
Paper • 2404.07503 • Published • 31 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 13 -
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50
-
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Paper • 2402.13064 • Published • 50 -
Textbooks Are All You Need II: phi-1.5 technical report
Paper • 2309.05463 • Published • 88 -
DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Paper • 2402.10379 • Published • 31 -
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Paper • 2312.06585 • Published • 29