5 papers
Discovering the Hidden Role of Gini Index In Prompt-based Classification
Ruixi Lin
In classification tasks, the long-tailed minority classes usually offer the predictions that are most important. Yet these classes consistently exhibit low accuracies, whereas a fe…
Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy
Ruixi Lin, Yang You
Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypica…
Ensemble Debiasing Across Class and Sample Levels for Fairer Prompting Accuracy
Ruixi Lin, Ziqiao Wang, Yang You
Language models are strong few-shot learners and achieve good overall accuracy in text classification tasks, masking the fact that their results suffer from great class accuracy im…
Let the Fuzzy Rule Speak: Enhancing In-context Learning Debiasing with Interpretability
Ruixi Lin, Yang You
Large language models (LLMs) often struggle with balanced class accuracy in text classification tasks using in-context learning (ICL), hindering some practical uses due to user dis…
Can a large language model be a gaslighter?
Wei Li, Luyao Zhu, Yang Song +3
Large language models (LLMs) have gained human trust due to their capabilities and helpfulness. However, this in turn may allow LLMs to affect users' mindsets by manipulating langu…