4 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models
Shikun Liu, Deyu Zou, Nima Shoghi +3
In the era of foundation models, fine-tuning pre-trained models for specific downstream tasks has become crucial. This drives the need for robust fine-tuning methods to address cha…
OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery
Vignesh Prabhakar, Md Amirul Islam, Adam Atanas +8
Large Language Models (LLMs) have demonstrated remarkable potential in advancing scientific knowledge and addressing complex challenges. In this work, we introduce OmniScience, a s…
A Modular Dataset to Demonstrate LLM Abstraction Capability
Adam Atanas, Kai Liu
Large language models (LLMs) exhibit impressive capabilities but struggle with reasoning errors due to hallucinations and flawed logic. To investigate their internal representation…
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
Xiangzhe Kong, Zishen Zhang, Ziting Zhang +5
The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restr…
Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation
Sadegh Mahdavi, Muchen Li, Kaiwen Liu +3
Advances in Large Language Models (LLMs) have sparked interest in their ability to solve Olympiad-level math problems. However, the training and evaluation of these models are cons…