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Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Paper • 2502.03460 • Published -
LLM-Pruner: On the Structural Pruning of Large Language Models
Paper • 2305.11627 • Published • 3 -
Pruning as a Domain-specific LLM Extractor
Paper • 2405.06275 • Published • 1 -
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Paper • 2402.11176 • Published • 2
Collections
Discover the best community collections!
Collections including paper arxiv:2402.05123
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A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development
Paper • 2407.11784 • Published • 4 -
Data Management For Large Language Models: A Survey
Paper • 2312.01700 • Published -
Datasets for Large Language Models: A Comprehensive Survey
Paper • 2402.18041 • Published • 2
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A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 54 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26
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PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Paper • 2310.17752 • Published • 15 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 32 -
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Paper • 2311.06243 • Published • 22 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30
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Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
Paper • 2307.06290 • Published • 10 -
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Paper • 2408.02085 • Published • 19 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Paper • 2403.14608 • Published
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How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
Paper • 2409.12941 • Published • 24 -
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper • 2503.24290 • Published • 62
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Effective pruning of web-scale datasets based on complexity of concept clusters
Paper • 2401.04578 • Published -
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
LESS: Selecting Influential Data for Targeted Instruction Tuning
Paper • 2402.04333 • Published • 3
-
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Paper • 2502.03460 • Published -
LLM-Pruner: On the Structural Pruning of Large Language Models
Paper • 2305.11627 • Published • 3 -
Pruning as a Domain-specific LLM Extractor
Paper • 2405.06275 • Published • 1 -
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Paper • 2402.11176 • Published • 2
-
Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
Paper • 2307.06290 • Published • 10 -
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Paper • 2408.02085 • Published • 19 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Paper • 2403.14608 • Published
-
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development
Paper • 2407.11784 • Published • 4 -
Data Management For Large Language Models: A Survey
Paper • 2312.01700 • Published -
Datasets for Large Language Models: A Comprehensive Survey
Paper • 2402.18041 • Published • 2
-
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
Paper • 2409.12941 • Published • 24 -
Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
Paper • 2503.24290 • Published • 62
-
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 54 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26
-
Effective pruning of web-scale datasets based on complexity of concept clusters
Paper • 2401.04578 • Published -
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 42 -
A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
LESS: Selecting Influential Data for Targeted Instruction Tuning
Paper • 2402.04333 • Published • 3
-
PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Paper • 2310.17752 • Published • 15 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 32 -
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Paper • 2311.06243 • Published • 22 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 30