5 papers
Extracting Small Translation Specialists from LLMs by Aggressively Pruning Experts
Liu O. Martin, Lucas Bandarkar, Nanyun Peng
Modern large language models (LLMs) achieve state-of-the-art machine translation performance, but they do so as broad generalists largely trained for many tasks and capabilities un…
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…
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…
On the Loss of Context-awareness in General Instruction Fine-tuning
Yihan Wang, Andrew Bai, Nanyun Peng +1
Pre-trained Large Language Models (LLMs) require post-training methods such as supervised fine-tuning (SFT) on instruction-response pairs to enable instruction following. However,…