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arXiv · 2410.12839

Capturing Bias Diversity in LLMs

Purva Prasad Gosavi·Vaishnavi Murlidhar Kulkarni·Alan F. Smeaton
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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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Purva Prasad Gosavi, Vaishnavi Murlidhar Kulkarni, Alan F. Smeaton. 2024-10-09. Capturing Bias Diversity in LLMs. https://doi.org/10.1109/fllm63129.2024.10852459

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