no code implementations • 2 May 2024 • Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, William Yang Wang
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance.
no code implementations • 2 Nov 2023 • Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, Linda Ruth Petzold
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details.
1 code implementation • 23 Oct 2023 • Xinlu Zhang, Chenxin Tian, Xianjun Yang, Lichang Chen, Zekun Li, Linda Ruth Petzold
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications.
1 code implementation • 22 May 2023 • Xinlu Zhang, Shiyang Li, Xianjun Yang, Chenxin Tian, Yao Qin, Linda Ruth Petzold
Although offering improved data privacy protection, domain-specific small language models (SLMs) often underperform LLMs, emphasizing the need for methods that reduce this performance gap while alleviating privacy concerns.
no code implementations • 16 Feb 2023 • Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, Wei Cheng
Text summarization has been a crucial problem in natural language processing (NLP) for several decades.
1 code implementation • 22 Oct 2022 • Xianjun Yang, Ya Zhuo, Julia Zuo, Xinlu Zhang, Stephen Wilson, Linda Petzold
Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction.
1 code implementation • 18 Oct 2022 • Xinlu Zhang, Shiyang Li, Zhiyu Chen, Xifeng Yan, Linda Petzold
Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism.
no code implementations • 13 Oct 2022 • Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, Wenhu Chen, Xifeng Yan
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.
no code implementations • 22 Jun 2021 • Xinlu Zhang, Yun Zhao, Rachael Callcut, Linda Petzold
Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients.
no code implementations • 19 Mar 2021 • Yun Zhao, Qinghang Hong, Xinlu Zhang, Yu Deng, Yuqing Wang, Linda Petzold
However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes.