3 citations · 11 across the 13 of their papers we have counts for
4 papers · 1 filter
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
Philip Kenneweg, Alexander Schulz, Sarah Schröder +1
Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on…
Debiasing Sentence Embedders through Contrastive Word Pairs
Philip Kenneweg, Sarah Schröder, Alexander Schulz +1
Over the last years, various sentence embedders have been an integral part in the success of current machine learning approaches to Natural Language Processing (NLP). Unfortunately…
The SAME score: Improved cosine based bias score for word embeddings
Sarah Schröder, Alexander Schulz, Barbara Hammer
With the enourmous popularity of large language models, many researchers have raised ethical concerns regarding social biases incorporated in such models. Several methods to measur…
Evaluating Metrics for Bias in Word Embeddings
Sarah Schröder, Alexander Schulz, Philip Kenneweg +3
Over the last years, word and sentence embeddings have established as text preprocessing for all kinds of NLP tasks and improved the performances significantly. Unfortunately, it h…