Search Results for author:
Found 7 papers, 3 papers with code
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
We quantify bias amplification in pretraining and after fine-tuning on three families of vision-and-language models.
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans.
PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge
We then evaluate its ability to acquire new knowledge and include it in its reasoning process.
Being Right for Whose Right Reasons?
Explainability methods are used to benchmark the extent to which model predictions align with human rationales i. e., are 'right for the right reasons'.
On the Independence of Association Bias and Empirical Fairness in Language Models
To this end, we first provide a thought experiment, showing how association bias and empirical fairness can be completely orthogonal.
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Detecting offensive language is a challenging task.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
+1
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue.
