LLM-Pruner: On the Structural Pruning of Large Language Models
Paper
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2305.11627
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Published
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3
Reading List
I add and I forget to read
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Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
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Pruning as a Domain-specific LLM Extractor
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KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
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LIMA: Less Is More for Alignment
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PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
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From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
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A Survey on Data Selection for LLM Instruction Tuning
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Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
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Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2
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Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models
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Scaling and evaluating sparse autoencoders
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Decomposing The Dark Matter of Sparse Autoencoders
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Test-Time Training on Video Streams
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DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization
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Domain Randomization via Entropy Maximization
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Efficient Estimation of Word Representations in Vector Space
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TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
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TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? - A Case Study on Korea Financial Texts
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Knowledge Distillation of Large Language Models
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Mentor-KD: Making Small Language Models Better Multi-step Reasoners
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Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation
Paper • 2505.19529 • Published • 4 -
Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
Paper • 2506.10952 • Published • 23 -
Resa: Transparent Reasoning Models via SAEs
Paper • 2506.09967 • Published • 22 -
LLM Unlearning Should Be Form-Independent
Paper • 2506.07795 • Published • 4 -
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
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