128 citations · 718 across the 52 of their papers we have counts for
7 papers · 2 filters
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…
Explainability for Large Language Models: A Survey
Haiyan Zhao, Hanjie Chen, Fan Yang +6
Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transpa…
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…
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
Saed Rezayi, Zhengliang Liu, Zihao Wu +8
This paper explores new frontiers in agricultural natural language processing by investigating the effectiveness of using food-related text corpora for pretraining transformer-base…
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…
NoPPA: Non-Parametric Pairwise Attention Random Walk Model for Sentence Representation
Xuansheng Wu, Zhiyi Zhao, Ninghao Liu
We propose a novel non-parametric/un-trainable language model, named Non-Parametric Pairwise Attention Random Walk Model (NoPPA), to generate sentence embedding only with pre-train…
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Xuansheng Wu, Xinyu He, Tianming Liu +2
Developing models to automatically score students' written responses to science problems is critical for science education. However, collecting and labeling sufficient student resp…