20 citations · 78 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
Ziyuan Wang, Hailiang Huang, Songqiao Han
Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in…
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…
American Hate Crime Trends Prediction with Event Extraction
Songqiao Han, Hailiang Huang, Jiangwei Liu +1
Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Cr…
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…