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Found 13 papers, 2 papers with code
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Your computer is continuously executing programs, but does it really understand them?
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process.
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
More than 200 generic drugs approved by the U. S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer.
Your fairness may vary: Pretrained language model fairness in toxic text classification
Through the analysis of more than a dozen pretrained language models of varying sizes on two toxic text classification tasks (English), we demonstrate that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics.
Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets
The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise.
Downstream Fairness Caveats with Synthetic Healthcare Data
In this paper, we evaluate the fairness of models generated on two healthcare datasets for gender and race biases.
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes.
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models
Bias auditing is further complicated by LM brittleness: when a presumably biased outcome is observed, is it due to model bias or model brittleness?
Subtle Misogyny Detection and Mitigation: An Expert-Annotated Dataset
Using novel approaches to dataset development, the Biasly dataset captures the nuance and subtlety of misogyny in ways that are unique within the literature.
SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models
Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender.
Fairness-Aware Structured Pruning in Transformers
The increasing size of large language models (LLMs) has introduced challenges in their training and inference.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
