60 citations · 115 across the 12 of their papers we have counts for
13 papers
ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference
Junda Wang, Zhichao Yang, Dongxu Zhang +2
Large reasoning models (LRMs) achieve state-of-the-art performance by generating long chains-of-thought, but often waste computation on redundant reasoning after the correct answer…
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
Junda Wang, Zonghai Yao, Avijit Mitra +3
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation sy…
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
Mengxue Zhang, Zichao Wang, Zhichao Yang +2
Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most exi…
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
Sunjae Kwon, Rishabh Garodia, Minhwa Lee +2
Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text…
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
Avijit Mitra, Richeek Pradhan, Rachel D Melamed +8
Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic he…
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models
Zhichao Yang, Weisong Liu, Dan Berlowitz +1
Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this progno…
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
Zhichao Yang, Sunjae Kwon, Zonghai Yao +1
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due t…
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning
Sunjae Kwon, Zhichao Yang, Hong Yu
In this paper, we introduce a comprehensive framework for developing a machine learning-based SOAP (Subjective, Objective, Assessment, and Plan) classification system without manua…
Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing
Zonghai Yao, Yi Cao, Zhichao Yang +1
Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for ga…
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
Zonghai Yao, Yi Cao, Zhichao Yang +2
Language Models (LMs) have performed well on biomedical natural language processing applications. In this study, we conducted some experiments to use prompt methods to extract know…
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding
Zhichao Yang, Shufan Wang, Bhanu Pratap Singh Rawat +2
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging d…
Generating Classical Chinese Poems from Vernacular Chinese
Zhichao Yang, Pengshan Cai, Yansong Feng +4
Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of genera…
Word Embedding Perturbation for Sentence Classification
Dongxu Zhang, Zhichao Yang
In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods. Specifically, we attempt several types of noise to p…