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  • Quantifying Bias and Fairness in LLMs - apxml. com
    Alignment efforts can inadvertently amplify existing societal biases present in the training data, or even introduce new ones Quantifying these biases allows us to understand the extent of the problem and measure the effectiveness of mitigation strategies
  • How to Assess Your LLM Use Case for Bias and Fairness with . . .
    To ensure the ethical deployment of these models, it is crucial to assess and mitigate bias through various testing methods Before we delve into the specifics of bias and fairness testing, let’s
  • Methods to Evaluate Bias in LLMs: Exploring 10 Fairness Metrics
    By quantifying bias in LLM outputs, bias detection metrics facilitate targeted interventions to mitigate bias For example, researchers curated a benchmark dataset consisting of news articles labeled for sentiment and bias
  • Exploring Bias Evaluation Techniques for Quantifying Large . . .
    This paper employs three internal bias metrics, namely SEAT, StereoSet, and CrowS Pairs, to evaluate nine bias involving gender, age, race, occupation, nationality, religion, sexual orientation, physical appearance and disability in five open source LLMs (Llama, Llama2, Alpaca, Vicuna, and MPT), thereby determining their specific bias level
  • Benchmarking Bias in Large Language Models during Role-Playing
    In this paper, we introduce BiasLens, a fairness testing framework designed to systematically expose biases in LLMs during role-playing Our approach uses LLMs to generate 550 social roles across a comprehensive set of 11 demographic attributes, producing 33,000 role-specific questions targeting various forms of bias
  • Unpacking the bias of large language models - MIT News
    MIT researchers discovered the underlying cause of position bias, a phenomenon that causes large language models to overemphasize the beginning or end of a document or conversation, while neglecting the middle They built a theoretical framework that can be used to diagnose and correct position bias in future model designs, leading to more accurate, reliable AI agents
  • Want to avoid bias in LLMs? Here are 4 strategies you need to . . .
    The data collection process can play a role in LLM bias since the datasets could be an overrepresentation of demographics and viewpoints that may be long outdated At the same time, data preprocessing and model training techniques may each play a role in bias


















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