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Found 5 papers, 1 papers with code
The Impact of Unstated Norms in Bias Analysis of Language Models
This approach is widely used in bias quantification.
FlexModel: A Framework for Interpretability of Distributed Large Language Models
With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase.
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.
