23 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models
Guangzhi Xiong, Eric Xie, Corey Williams +5
Large language models (LLMs) have shown significant potential in scientific disciplines such as biomedicine, particularly in hypothesis generation, where they can analyze vast lite…
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…
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…