Summary: | The Large Language Model Bias Index (LLMBI) is a pioneering approach designed to quantify and address biases
inherent in large language models (LLMs), such as GPT-4.
We recognise the increasing prevalence and impact of LLMs
across diverse sectors. This research introduces a novel metric, LLMBI, to systematically measure and mitigate biases
potentially skewing model responses. We formulated LLMBI
using a composite scoring system incorporating multiple
dimensions of bias, including but not limited to age, gender,
and racial biases.
To operationalise this metric, we engaged in a multi-step
process involving collecting and annotating LLM responses,
applying sophisticated Natural Language Processing (NLP)
techniques for bias detection, and computing the LLMBI
score through a specially crafted mathematical formula. The
formula integrates weighted averages of various bias dimensions, a penalty for dataset diversity deficiencies, and
a correction for sentiment biases. Our empirical analysis,
conducted using responses from OpenAI’s API, employs advanced sentiment analysis as a representative method for
bias detection.
The research reveals LLMs, whilst demonstrating impressive
capabilities in text generation, exhibit varying degrees of
bias across different dimensions. LLMBI provides a quantifiable measure to compare biases across models and over time,
offering a vital tool for systems engineers, researchers and
regulators in enhancing the fairness and reliability of LLMs.
It highlights the potential of LLMs in mimicking unbiased
human-like responses. Additionally, it underscores the necessity of continuously monitoring and recalibrating such
models to align with evolving societal norms and ethical
standards.
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