no code implementations • 17 Oct 2024 • Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang, Zhiyu Zoey Chen
There is a significant gap between patient needs and available mental health support today.
no code implementations • 3 Aug 2024 • Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
To solve this joint optimization problem, we perform a convergence analysis on the gradient $l_2$-norm of the asynchronous multi-tier federated learning (FL) model with adaptive model pruning.
no code implementations • 30 May 2024 • Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye, Xianjun Yang, Lichang Chen, William Yang Wang, Linda Ruth Petzold
First, coding data tuning enhances the overall reasoning capabilities of LLMs across different model families and scales.
1 code implementation • 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.