8 citations · 24 across the 7 of their papers we have counts for
10 papers
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation
Chenlu Ding, Jiancan Wu, Yanchen Luo +4
Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target information w…
Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning
Chenlu Ding, Jiancan Wu, Yanchen Luo +3
Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became availab…
3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
Chang Wu, Zhiyuan Liu, Wen Shu +6
Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL).However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial,…
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…
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
Zhiyuan Liu, Yanchen Luo, Han Huang +7
3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers,…
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…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
Zhiyuan Liu, Sihang Li, Yanchen Luo +5
Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical a…
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang +6
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a cru…