SELF: Language-Driven Self-Evolution for Large Language Model
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2310.00533
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Published
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2
Papers - Training
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GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length
Paper • 2310.00576 • Published • 2 -
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Paper • 2305.13169 • Published • 3 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 13 -
Triple-Encoders: Representations That Fire Together, Wire Together
Paper • 2402.12332 • Published • 2 -
Veagle: Advancements in Multimodal Representation Learning
Paper • 2403.08773 • Published • 7 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 10 -
Hash Layers For Large Sparse Models
Paper • 2106.04426 • Published • 2 -
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 38 -
Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 37 -
Qwen2 Technical Report
Paper • 2407.10671 • Published • 160 -
Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
Paper • 2404.05405 • Published • 9 -
Scaling Laws for Precision
Paper • 2411.04330 • Published • 6 -
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Paper • 1806.07572 • Published • 1 -
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
Paper • 2411.12580 • Published • 2 -
Studying Large Language Model Generalization with Influence Functions
Paper • 2308.03296 • Published • 12 -
Scaling and evaluating sparse autoencoders
Paper • 2406.04093 • Published • 3 -
SONAR: Sentence-Level Multimodal and Language-Agnostic Representations
Paper • 2308.11466 • Published • 1 -
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Paper • 2105.13626 • Published • 3 -
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Paper • 2103.06874 • Published • 1 -
Phi-4 Technical Report
Paper • 2412.08905 • Published • 92 -
An Evolved Universal Transformer Memory
Paper • 2410.13166 • Published • 3 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 41 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 75 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3