13 citations · 42 across the 18 of their papers we have counts for
7 papers · 1 filter
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei +6
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the poten…
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Chunjing Gan, Dan Yang, Binbin Hu +9
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucinati…
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Lei Liu, Xiaoyan Yang, Fangzhou Li +10
Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural la…
Unified Hallucination Detection for Multimodal Large Language Models
Xiang Chen, Chenxi Wang, Yida Xue +7
Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such halluci…
RJUA-QA: A Comprehensive QA Dataset for Urology
Shiwei Lyu, Chenfei Chi, Hongbo Cai +14
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models…
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
Qiang Li, Xiaoyan Yang, Haowen Wang +14
Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
Junjie Wang, Dan Yang, Binbin Hu +3
In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this is…