Large Language Model (LLM) Bias Index -- LLMBI
- URL: http://arxiv.org/abs/2312.14769v3
- Date: Fri, 29 Dec 2023 11:07:09 GMT
- Title: Large Language Model (LLM) Bias Index -- LLMBI
- Authors: Abiodun Finbarrs Oketunji, Muhammad Anas, Deepthi Saina
- Abstract summary: The Large Language Model Bias Index (LLMBI) is a pioneering approach designed to quantify and address biases inherent in large language models (LLMs)
We formulated LLMBI using a composite scoring system incorporating multiple dimensions of bias, including but not limited to age, gender, and racial biases.
Our empirical analysis, conducted using responses from OpenAI's API, employs advanced sentiment analysis as a representative method for bias detection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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