301 citations · 316 across the 6 of their papers we have counts for
6 papers
Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMs
Biyang Guo, He Wang, Wenyilin Xiao +4
In the burgeoning field of Large Language Models (LLMs) like ChatGPT and LLaMA, Prompt Engineering (PE) is renowned for boosting zero-shot or in-context learning (ICL) through prom…
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Biyang Guo, Xin Zhang, Ziyuan Wang +5
The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions…
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
Biyang Guo, Yeyun Gong, Yelong Shen +4
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual s…
Selective Text Augmentation with Word Roles for Low-Resource Text Classification
Biyang Guo, Songqiao Han, Hailiang Huang
Data augmentation techniques are widely used in text classification tasks to improve the performance of classifiers, especially in low-resource scenarios. Most previous methods con…
What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification
Biyang Guo, Sonqiao Han, Hailiang Huang
Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios. Whilst lots of creativ…
Label Confusion Learning to Enhance Text Classification Models
Biyang Guo, Songqiao Han, Xiao Han +2
Representing a true label as a one-hot vector is a common practice in training text classification models. However, the one-hot representation may not adequately reflect the relati…