In-Context Learning of Energy Functions
Fuente:
arXiv
Saved in:
| Main Authors: | Schaeffer, Rylan, Khona, Mikail, Koyejo, Sanmi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Pretraining Scaling Laws for Generative Evaluations of Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Frontier AI Models
by: Duan, Sunny, et al.
Published: (2024)
by: Duan, Sunny, 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)
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
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)
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)
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)
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment
by: Obbad, Elyas, et al.
Published: (2024)
by: Obbad, Elyas, et al.
Published: (2024)
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)
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)
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations
by: Schaeffer, Rylan, et al.
Published: (2024)
by: Schaeffer, Rylan, et al.
Published: (2024)
Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)
by: Jiang, Minhao, 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)
Quantifying Variance in Evaluation Benchmarks
by: Madaan, Lovish, et al.
Published: (2024)
by: Madaan, Lovish, et al.
Published: (2024)
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)
Causally Inspired Regularization Enables Domain General Representations
by: Salaudeen, Olawale, et al.
Published: (2024)
by: Salaudeen, Olawale, et al.
Published: (2024)
Scale Dependent Data Duplication
by: Kazdan, Joshua, et al.
Published: (2026)
by: Kazdan, Joshua, et al.
Published: (2026)
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)
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)
How Do Large Language Monkeys Get Their Power (Laws)?
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)
by: Hughes, John, et al.
Published: (2024)
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
by: Ramesh, Rahul, et al.
Published: (2023)
by: Ramesh, Rahul, et al.
Published: (2023)
Discovering Implicit Large Language Model Alignment Objectives
by: Chen, Edward, et al.
Published: (2026)
by: Chen, Edward, et al.
Published: (2026)
Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)
by: Allouah, Youssef, 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)
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)
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing
by: Nishi, Kento, et al.
Published: (2024)
by: Nishi, Kento, 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)
High-Dimensional Markov-switching Ordinary Differential Processes
by: Tsai, Katherine, et al.
Published: (2024)
by: Tsai, Katherine, et al.
Published: (2024)
A Framework for Objective-Driven Dynamical Stochastic Fields
by: Zhang, Yibo Jacky, et al.
Published: (2025)
by: Zhang, Yibo Jacky, 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)
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds
by: Chen, Edward, et al.
Published: (2025)
by: Chen, Edward, et al.
Published: (2025)
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?
by: Salaudeen, Olawale, et al.
Published: (2025)
by: Salaudeen, Olawale, et al.
Published: (2025)
Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)
by: Wang, Xiaoyang, et al.
Published: (2022)
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Why Do Safety Guardrails Degrade Across Languages?
by: Zhang, Max, et al.
Published: (2026)
by: Zhang, Max, et al.
Published: (2026)
Logits are All We Need to Adapt Closed Models
by: Hiranandani, Gaurush, et al.
Published: (2025)
by: Hiranandani, Gaurush, et al.
Published: (2025)
Similar Items
-
Pretraining Scaling Laws for Generative Evaluations of Language Models
by: Schaeffer, Rylan, et al.
Published: (2025) -
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Frontier AI Models
by: Duan, Sunny, et al.
Published: (2024) -
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025) -
Bridging Associative Memory and Probabilistic Modeling
by: Schaeffer, Rylan, et al.
Published: (2024) -
Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
by: Gupta, Isha, et al.
Published: (2025)