scaling law

Collaborative Performance Prediction for Large Language Models EMNLP 2024 D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models NeurIPS 2024 Do Large Language Models Know How Much They Know? EMNLP 2024 Emergent Abilities in Reduced-Scale Generative Language Models NAACL 2024 Exploring Molecular Pretraining Model at Scale NeurIPS 2024 fMRI predictors based on language models of increasing complexity recover brain left lateralization NeurIPS 2024 Language Modeling Is Compression ICLR 2024 Measuring Cross-lingual Transfer in Bytes NAACL 2024 Mechanistic Design and Scaling of Hybrid Architectures ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL ICML 2024 Navigating Scaling Laws: Compute Optimality in Adaptive Model Training ICML 2024 No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance NeurIPS 2024 Observational Scaling Laws and the Predictability of Langauge Model Performance NeurIPS 2024 On the Scalability of Diffusion-based Text-to-Image Generation CVPR 2024 On the Scaling Laws of Geographical Representation in Language Models COLING 2024 Predicting Emergent Abilities with Infinite Resolution Evaluation ICLR 2024 Resolving Discrepancies in Compute-Optimal Scaling of Language Models NeurIPS 2024 Scaling Behavior for Large Language Models regarding Numeral Systems: An Example using Pythia EMNLP 2024 ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws EMNLP 2024 Scaling Law for Time Series Forecasting NeurIPS 2024 Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models EMNLP 2024 Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations NeurIPS 2024 Scaling Laws for Associative Memories ICLR 2024 Scaling Laws for Data Filtering-- Data Curation cannot be Compute Agnostic CVPR 2024 Scaling Laws for Fact Memorization of Large Language Models EMNLP 2024