8 citations · 21 across the 12 of their papers we have counts for
5 papers · 1 filter
Interpretable Reward Model via Sparse Autoencoder
Shuyi Zhang, Wei Shi, Sihang Li +3
Large language models (LLMs) have been widely deployed across numerous fields. Reinforcement Learning from Human Feedback (RLHF) leverages reward models (RMs) as proxies for human…
Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
Yanchen Luo, Zhiyuan Liu, Yi Zhao +6
3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordi…
Route Sparse Autoencoder to Interpret Large Language Models
Wei Shi, Sihang Li, Tao Liang +4
Mechanistic interpretability of large language models (LLMs) aims to uncover the internal processes of information propagation and reasoning. Sparse autoencoders (SAEs) have demons…
Text-guided Diffusion Model for 3D Molecule Generation
Yanchen Luo, Junfeng Fang, Sihang Li +5
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property va…
Towards 3D Molecule-Text Interpretation in Language Models
Sihang Li, Zhiyuan Liu, Yanchen Luo +5
Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potent…