Pretraining Scaling Laws for Generative Evaluations of Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Schaeffer, Rylan, Levi, Noam, Miranda, Brando, Koyejo, Sanmi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023)
by: Miranda, Brando, et al.
Published: (2023)
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)
by: Obbad, Elyas, et al.
Published: (2024)
Scale Dependent Data Duplication
by: Kazdan, Joshua, et al.
Published: (2026)
by: Kazdan, Joshua, et al.
Published: (2026)
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Quantifying the Effect of Test Set Contamination on Generative Evaluations
by: Schaeffer, Rylan, et al.
Published: (2026)
by: Schaeffer, Rylan, et al.
Published: (2026)
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)
by: Gupta, Isha, et al.
Published: (2025)
Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)
by: Jiang, Minhao, et al.
Published: (2024)
How Do Large Language Monkeys Get Their Power (Laws)?
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
A Simple Model of Inference Scaling Laws
by: Levi, Noam
Published: (2024)
by: Levi, Noam
Published: (2024)
What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
by: Lecomte, Victor, et al.
Published: (2023)
by: Lecomte, Victor, et al.
Published: (2023)
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
by: Kazdan, Joshua, et al.
Published: (2024)
by: Kazdan, Joshua, et al.
Published: (2024)
Quantifying Variance in Evaluation Benchmarks
by: Madaan, Lovish, et al.
Published: (2024)
by: Madaan, Lovish, et al.
Published: (2024)
Scaling Laws for Downstream Task Performance of Large Language Models
by: Isik, Berivan, et al.
Published: (2024)
by: Isik, Berivan, et al.
Published: (2024)
Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulness
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
by: Denisov-Blanch, Yegor, et al.
Published: (2026)
Causally Inspired Regularization Enables Domain General Representations
by: Salaudeen, Olawale, et al.
Published: (2024)
by: Salaudeen, Olawale, et al.
Published: (2024)
Let's Measure Information Step-by-Step: AI-Based Evaluation Beyond Vibes
by: Robertson, Zachary, et al.
Published: (2025)
by: Robertson, Zachary, et al.
Published: (2025)
No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
Discovering Implicit Large Language Model Alignment Objectives
by: Chen, Edward, et al.
Published: (2026)
by: Chen, Edward, et al.
Published: (2026)
Is Pre-training Truly Better Than Meta-Learning?
by: Miranda, Brando, et al.
Published: (2023)
by: Miranda, Brando, et al.
Published: (2023)
Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4
by: Aniva, Leni, et al.
Published: (2024)
by: Aniva, Leni, et al.
Published: (2024)
Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Quantifying the Importance of Data Alignment in Downstream Model Performance
by: Chawla, Krrish, et al.
Published: (2025)
by: Chawla, Krrish, et al.
Published: (2025)
The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets
by: Levi, Noam, et al.
Published: (2023)
by: Levi, Noam, et al.
Published: (2023)
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Lean-ing on Quality: How High-Quality Data Beats Diverse Multilingual Data in AutoFormalization
by: Chan, Willy, et al.
Published: (2025)
by: Chan, Willy, et al.
Published: (2025)
Learning Shrinks the Hard Tail: Training-Dependent Inference Scaling in a Solvable Linear Model
by: Levi, Noam
Published: (2026)
by: Levi, Noam
Published: (2026)
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)
by: Hughes, John, et al.
Published: (2024)
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Reasoning Models Don't Just Think Longer, They Move Differently
by: Gjølbye, Anders, et al.
Published: (2026)
by: Gjølbye, Anders, et al.
Published: (2026)
HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
by: Zhu, Junzhe, et al.
Published: (2023)
by: Zhu, Junzhe, et al.
Published: (2023)
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?
by: Salaudeen, Olawale, et al.
Published: (2025)
by: Salaudeen, Olawale, et al.
Published: (2025)
Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)
by: Allouah, Youssef, et al.
Published: (2025)
Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency
by: Zhang, Yibo Jacky, et al.
Published: (2026)
by: Zhang, Yibo Jacky, et al.
Published: (2026)
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
by: Jiang, Enyi, et al.
Published: (2023)
by: Jiang, Enyi, et al.
Published: (2023)
Similar Items
-
Evaluating the Robustness of Chinchilla Compute-Optimal Scaling
by: Schaeffer, Rylan, et al.
Published: (2025) -
Efficient Prediction of Pass@k Scaling in Large Language Models
by: Kazdan, Joshua, et al.
Published: (2025) -
In-Context Learning of Energy Functions
by: Schaeffer, Rylan, et al.
Published: (2024) -
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
by: Miranda, Brando, et al.
Published: (2023) -
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)