no code implementations • 2 Feb 2024 • Haochun Wang, Sendong Zhao, Zewen Qiang, Bing Qin, Ting Liu
In the field of natural language processing (NLP), Large Language Models (LLMs) have precipitated a paradigm shift, markedly enhancing performance in natural language generation tasks.
Multiple-choice Multiple Choice Question Answering (MCQA) +1
no code implementations • 29 Jan 2024 • Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, Ting Liu
Automatic diagnosis is a significant application of AI in healthcare, where diagnoses are generated based on the symptom description of patients.
no code implementations • 20 Oct 2023 • Yanrui Du, Sendong Zhao, Haochun Wang, Yuhan Chen, Rui Bai, Zewen Qiang, MuZhen Cai, Bing Qin
Through extensive experiments on five reasoning datasets from the ERASER benchmark, we demonstrate that our framework not only establishes a more reliable link between the generated rationale and model decision but also achieves competitive results in task performance and the quality of rationale.
1 code implementation • 8 Sep 2023 • Haochun Wang, Sendong Zhao, Zewen Qiang, Zijian Li, Nuwa Xi, Yanrui Du, MuZhen Cai, Haoqiang Guo, Yuhan Chen, Haoming Xu, Bing Qin, Ting Liu
To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation.
1 code implementation • 14 Apr 2023 • Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, Ting Liu
Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks.