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Found 5 papers, 5 papers with code
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data.
Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning
The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials.
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning?
Mitigating Biases in Toxic Language Detection through Invariant Rationalization
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse.
Cooperative Self-training of Machine Reading Comprehension
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings.
Ranked #1 on
Question Answering
on MRQA out-of-domain
Extractive Question-Answering
Machine Reading Comprehension
+6
