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Found 6 papers, 5 papers with code
Label Confusion Learning to Enhance Text Classification Models
LCM can learn label confusion to capture semantic overlap among labels by calculating the similarity between instances and labels during training and generate a better label distribution to replace the original one-hot label vector, thus improving the final classification performance.
What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification
Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios.
Selective Text Augmentation with Word Roles for Low-Resource Text Classification
Different words may play different roles in text classification, which inspires us to strategically select the proper roles for text augmentation.
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
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 spans, phrases, or words, concatenated by mask tokens).
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMs
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 prompt modifications.
