99 citations · 207 across the 24 of their papers we have counts for
7 papers · 1 filter
Foundation Models for Low-Resource Language Education (Vision Paper)
Zhaojun Ding, Zhengliang Liu, Hanqi Jiang +4
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, t…
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
Peng Shu, Junhao Chen, Zhengliang Liu +13
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of tasks and domains. However, their performance in low-resource language translation, particu…
Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
Hao Zhen, Yucheng Shi, Yongcan Huang +2
Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash s…
Mitigating Shortcuts in Language Models with Soft Label Encoding
Zirui He, Huiqi Deng, Haiyan Zhao +2
Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. In this work, we aim to answer the fo…
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Zihan Guan, Zihao Wu, Zhengliang Liu +5
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in c…
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Haixing Dai, Zhengliang Liu, Wenxiong Liao +15
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially p…