Naomi Saphra
  • Bio
  • Posts
  • Publications
  • Publications
    • How to visualize training dynamics in neural networks
    • PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs
    • Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon
    • Distributional Scaling Laws for Emergent Capabilities
    • Sometimes I am a Tree: Data drives fragile hierarchical generalization
    • Attribute Diversity Determines the Systematicity Gap in VQA
    • Benchmarks as Microscopes: A Call for Model Metrology
    • Causation Does Not Imply Correlation: A Study of Circuit Mechanisms and Model Behaviors
    • ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context
    • Dynamic Masking Rate Schedules for MLM Pretraining
    • Fast Forwarding Low-Rank Training
    • First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models
    • Loss in the Crowd: Hidden Breakthroughs in Language Model Training
    • Mechanistic?
    • Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
    • TRAM: Bridging Trust Regions and Sharpness Aware Minimization
    • Transcendence: Generative Models Can Outperform The Experts That Train Them
    • Understanding biological active sensing behaviors by interpreting learned artificial agent policies
    • Delays, Detours, and Forks in the Road: Latent State Models of Training Dynamics
    • Interpretability Creationism
    • Linear Connectivity Reveals Generalization Strategies
    • Shapley Interactions for Complex Feature Attribution
    • State-of-the-art generalisation research in NLP: a taxonomy and review
    • Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
    • Learning Transductions to Test Systematic Compositionality
    • One Venue, Two Conferences: The Separation of Chinese and American Citation Networks
    • The MultiBERTs: BERT Reproductions for Robustness Analysis
    • A Non-Linear Structural Probe
    • LSTMs Compose---and Learn---Bottom-Up
    • Pareto Probing: Trading Off Accuracy for Complexity
    • Understanding Privacy-Related Questions on Stack Overflow
    • Carbon AI and the Concentration of Computational Work
    • Sparsity Emerges Naturally in Neural Language Models
    • Understanding Learning Dynamics Of Language Models with SVCCA
    • DyNet: The Dynamic Neural Network Toolkit
    • Evaluating Informal-Domain Word Representations with UrbanDictionary
    • AMRICA: an AMR Inspector for Cross-language Alignments
    • A framework for (under) specifying dependency syntax without overloading annotators
    • An Algerian Arabic-French Code-Switched Corpus
    • Understanding Objects in Detail with Fine-grained Attributes
  • Blog
    • The Parable of the Prinia's Egg: An Allegory for AI Science
    • Interpretability Creationism
    • Against Monodomainism
    • What Does a Coder Do If They Can't Type?
    • Model Scheduling
    • Understanding Latent Dirichlet Allocation

Distributional Scaling Laws for Emergent Capabilities

Mar 1, 2025·
Rosie Zhao
,
Tian Qin
,
David Alvarez-Melis
,
Sham Kakade
Naomi Saphra
Naomi Saphra
· 0 min read
Cite URL
Type
Preprint
Last updated on Mar 1, 2025
Large Language Models Science of Deep Learning Compositionality
Naomi Saphra
Authors
Naomi Saphra
Research Fellow
← Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon May 1, 2025
Sometimes I am a Tree: Data drives fragile hierarchical generalization Feb 1, 2025 →

© 2025 Me. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.