23 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models
Guangzhi Xiong, Eric Xie, Amir Hassan Shariatmadari +3
Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their abili…
IdeaBench: Benchmarking Large Language Models for Research Idea Generation
Sikun Guo, Amir Hassan Shariatmadari, Guangzhi Xiong +4
Large Language Models (LLMs) have transformed how people interact with artificial intelligence (AI) systems, achieving state-of-the-art results in various tasks, including scientif…
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions
Guangzhi Xiong, Qiao Jin, Xiao Wang +3
The emergent abilities of large language models (LLMs) have demonstrated great potential in solving medical questions. They can possess considerable medical knowledge, but may stil…
A Self-explaining Neural Architecture for Generalizable Concept Learning
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process. Concept learning…
Benchmarking Retrieval-Augmented Generation for Medicine
Guangzhi Xiong, Qiao Jin, Zhiyong Lu +1
While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinatio…