Search Results for author: Xinlu Zhang

Found 13 papers, 5 papers with code

Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning

no code implementations3 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.

Federated Learning

Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning

no code implementations30 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.

A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

1 code implementation2 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.

Ethics

GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks

no code implementations2 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.

Image Generation Image to text

AlpaCare:Instruction-tuned Large Language Models for Medical Application

1 code implementation23 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.

Diversity Instruction Following

Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting

1 code implementation22 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.

Decision Making Privacy Preserving

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

no code implementations16 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.

Abstractive Text Summarization

PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text

1 code implementation22 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.

Named Entity Recognition Named Entity Recognition (NER) +3

Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling

1 code implementation18 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.

Imputation Irregular Time Series +2

Explanations from Large Language Models Make Small Reasoners Better

no code implementations13 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.

Explanation Generation In-Context Learning +1

Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks

no code implementations22 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.

Imputation Missing Values

BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients

no code implementations19 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.

Deep Learning Mortality Prediction +1

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