1 citations · 1 across the 4 of their papers we have counts for
8 papers
How to Make Large Language Models Generate 100% Valid Molecules?
Wen Tao, Jing Tang, Alvin Chan +5
Molecule generation is key to drug discovery and materials science, enabling the design of novel compounds with specific properties. Large language models (LLMs) can learn to perfo…
Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese Drugs
Sifan Li, Yujun Cai, Bryan Hooi +2
Traditional Chinese Medicine (TCM) has seen increasing adoption in healthcare, with specialized Large Language Models (LLMs) emerging to support clinical applications. A fundamenta…
Enhancing LLM Character-Level Manipulation via Divide and Conquer
Zhen Xiong, Yujun Cai, Bryan Hooi +3
Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks. However, they exhibit notable weak…
Structured Outputs Enable General-Purpose LLMs to be Medical Experts
Guangfu Guo, Kai Zhang, Bryan Hoo +4
Medical question-answering (QA) is a critical task for evaluating how effectively large language models (LLMs) encode clinical knowledge and assessing their potential applications…
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling
Bingxuan Li, Yiwei Wang, Jiuxiang Gu +2
Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has great potential to empower the autom…
Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments
Jiaxi Li, Yiwei Wang, Kai Zhang +5
Large language models (LLMs) have been widely adopted in various downstream task domains. However, their abilities to directly recall and apply factual medical knowledge remains un…