360 citations · 478 across the 11 of their papers we have counts for
19 papers · 1 filter
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting
Lei Shu, Liangchen Luo, Jayakumar Hoskere +5
Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks such as storytelling and E-mail generation. However, as LLMs are primarily trained on final…
Adapting a Language Model While Preserving its General Knowledge
Zixuan Ke, Yijia Shao, Haowei Lin +3
Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a…
Continual Training of Language Models for Few-Shot Learning
Zixuan Ke, Haowei Lin, Yijia Shao +3
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can pr…
Zero-Shot Aspect-Based Sentiment Analysis
Lei Shu, Hu Xu, Bing Liu +1
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new…
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
Hu Xu, Lei Shu, Philip S. Yu +1
This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent pro…
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Hu Xu, Bing Liu, Lei Shu +1
Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classif…