89 citations · 196 across the 12 of their papers we have counts for
12 papers
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
Mian Zhang, Xianjun Yang, Xinlu Zhang +6
There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models…
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye +4
Instruction Fine-Tuning (IFT) significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). While coding data is known to boost LLM reasoning abilit…
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang +7
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: dom…
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Xinlu Zhang, Yujie Lu, Weizhi Wang +7
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details. Alt…
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Xinlu Zhang, Chenxin Tian, Xianjun Yang +3
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications. However, pr…
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Xinlu Zhang, Shiyang Li, Xianjun Yang +3
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data…
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Xianjun Yang, Yan Li, Xinlu Zhang +2
Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining…
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
Xianjun Yang, Ya Zhuo, Julia Zuo +3
Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction. But the lack of annotat…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Xinlu Zhang, Shiyang Li, Zhiyu Chen +2
Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequ…
Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks
Xinlu Zhang, Yun Zhao, Rachael Callcut +1
Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Early and precise detection is critical for clinicians to mak…
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
Yun Zhao, Qinghang Hong, Xinlu Zhang +3
Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, d…