Enhancing Large Language Model Self-Reflection to Detect and Mitigate Biases
Equipping large language models with mechanisms for better self-reflection and bias recognition can significantly improve their capability to identify and address biases in their outputs.
Comprehensive Bias Neutralization Framework: Measuring and Mitigating Racial, Cultural, and Gender Biases in Large Language Models
The Comprehensive Bias Neutralization Framework (CBNF) introduces a novel metric called Bias Intelligence Quotient (BiQ) to detect, measure, and mitigate racial, cultural, and gender biases in Large Language Models (LLMs), with a focus on Retrieval Augmented Generation (RAG) models.
SAGED: A Comprehensive Bias Benchmarking Pipeline for Customizable Fairness Evaluation of Large Language Models
SAGED is a novel, holistic, and highly customizable pipeline that enables comprehensive bias detection and mitigation for large language models, addressing the limitations of existing benchmarks.
Evaluating Bias Sensitivity in Large Language Models through Progressively Offensive Scenarios
Large Language Models (LLMs) exhibit varying degrees of bias, which can lead to harmful outputs. The Sensitivity Testing on Offensive Progressions (STOP) dataset provides a novel framework to assess the complex nature of biases in LLMs, enabling more effective bias mitigation strategies and the creation of fairer language models.
Uncovering Implicit Biases in Large Language Models: An Interdisciplinary Investigation of Algorithmic Prejudices in Open-Ended Text Generation
Large Language Models (LLMs) trained on internet data reflect and perpetuate societal biases related to gender, race, and culture, which can have significant societal impacts when these models are deployed in various applications.
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