1 citations · 4 across the 12 of their papers we have counts for
13 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…
DRS: Deep Question Reformulation With Structured Output
Zhecheng Li, Yiwei Wang, Bryan Hooi +3
Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions t…
Control Large Language Models via Divide and Conquer
Bingxuan Li, Yiwei Wang, Tao Meng +2
This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG). We systematically ev…
Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Cross-lingual summarization (CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at…
Vulnerability of LLMs to Vertically Aligned Text Manipulations
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Vertical text input is commonly encountered in various real-world applications, such as mathematical computations and word-based Sudoku puzzles. While current large language models…
Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding
Cheng Wang, Yiwei Wang, Bryan Hooi +3
The training data in large language models is key to their success, but it also presents privacy and security risks, as it may contain sensitive information. Detecting pre-training…
LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning
Silin Meng, Yiwei Wang, Cheng-Fu Yang +2
Path planning is a fundamental scientific problem in robotics and autonomous navigation, requiring the derivation of efficient routes from starting to destination points while avoi…
DeepEdit: Knowledge Editing as Decoding with Constraints
Yiwei Wang, Muhao Chen, Nanyun Peng +1
How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs). The difficulty arises because the ha…