Capturing Bias Diversity in LLMs

• URL: http://arxiv.org/abs/2410.12839v1
• Date: Wed, 09 Oct 2024 17:07:50 GMT
• Title: Capturing Bias Diversity in LLMs
• Authors: Purva Prasad Gosavi, Vaishnavi Murlidhar Kulkarni, Alan F. Smeaton,
• Abstract summary: This paper presents research on enhancements to Large Language Models (LLMs) through the addition of diversity in its generated outputs. By developing multiple customised instances of a GPT model, each reflecting biases in specific demographic characteristics including gender, age, and race, we propose, develop and evaluate a framework for a more nuanced and representative AI dialogue which we call BiasGPT. In this paper, through experiments, we demonstrate the capabilities of a GPT model to embed different biases which, when combined, can open the possibilities of more inclusive AI technologies.
• Score: 1.9685736810241874
• License:
• Abstract: This paper presents research on enhancements to Large Language Models (LLMs) through the addition of diversity in its generated outputs. Our study introduces a configuration of multiple LLMs which demonstrates the diversities capable with a single LLM. By developing multiple customised instances of a GPT model, each reflecting biases in specific demographic characteristics including gender, age, and race, we propose, develop and evaluate a framework for a more nuanced and representative AI dialogue which we call BiasGPT. The customised GPT models will ultimately collaborate, merging their diverse perspectives on a topic into an integrated response that captures a broad spectrum of human experiences and viewpoints. In this paper, through experiments, we demonstrate the capabilities of a GPT model to embed different biases which, when combined, can open the possibilities of more inclusive AI technologies.

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