Train Sparse Autoencoders Efficiently by Utilizing Features Correlation Paper • 2505.22255 • Published 5 days ago • 20
Train Sparse Autoencoders Efficiently by Utilizing Features Correlation Paper • 2505.22255 • Published 5 days ago • 20
You Do Not Fully Utilize Transformer's Representation Capacity Paper • 2502.09245 • Published Feb 13 • 38
You Do Not Fully Utilize Transformer's Representation Capacity Paper • 2502.09245 • Published Feb 13 • 38
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models Paper • 2502.03032 • Published Feb 5 • 61
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models Paper • 2502.03032 • Published Feb 5 • 61
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models Paper • 2502.03032 • Published Feb 5 • 61 • 2
The Differences Between Direct Alignment Algorithms are a Blur Paper • 2502.01237 • Published Feb 3 • 115
The Differences Between Direct Alignment Algorithms are a Blur Paper • 2502.01237 • Published Feb 3 • 115
Mechanistic Permutability: Match Features Across Layers Paper • 2410.07656 • Published Oct 10, 2024 • 19
Mechanistic Permutability: Match Features Across Layers Paper • 2410.07656 • Published Oct 10, 2024 • 19 • 2
Mechanistic Permutability: Match Features Across Layers Paper • 2410.07656 • Published Oct 10, 2024 • 19
Classifiers are Better Experts for Controllable Text Generation Paper • 2205.07276 • Published May 15, 2022
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM Paper • 2406.12168 • Published Jun 18, 2024 • 7
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning Paper • 2406.08973 • Published Jun 13, 2024 • 90
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning Paper • 2101.04229 • Published Jan 11, 2021