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Found 9 papers, 1 papers with code
The Impact of Unstated Norms in Bias Analysis of Language Models
This approach is widely used in bias quantification.
Interpretable Stereotype Identification through Reasoning
Given that language models are trained on vast datasets that may contain inherent biases, there is a potential danger of inadvertently perpetuating systemic discrimination.
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the learned or inherited social biases of these models.
Soft-prompt Tuning for Large Language Models to Evaluate Bias
In this paper, we explore the use of soft-prompt tuning on sentiment classification task to quantify the biases of large language models (LLMs) such as Open Pre-trained Transformers (OPT) and Galactica language model.
MLHOps: Machine Learning for Healthcare Operations
We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools).
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology.
An Experimental Evaluation of Transformer-based Language Models in the Biomedical Domain
With the growing amount of text in health data, there have been rapid advances in large pre-trained models that can be applied to a wide variety of biomedical tasks with minimal task-specific modifications.
Extracting relevant information from physician-patient dialogues for automated clinical note taking
We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients.
Predicting ICU transfers using text messages between nurses and doctors
We explore the use of real-time clinical information, i. e., text messages sent between nurses and doctors regarding patient conditions in order to predict transfer to the intensive care unit(ICU).
