Capturing Bias Diversity in LLMs
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908476151693312 |
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| author | Gosavi, Purva Prasad Kulkarni, Vaishnavi Murlidhar Smeaton, Alan F. |
| author_facet | Gosavi, Purva Prasad Kulkarni, Vaishnavi Murlidhar Smeaton, Alan F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Capturing Bias Diversity in LLMs Gosavi, Purva Prasad Kulkarni, Vaishnavi Murlidhar Smeaton, Alan F. Computation and Language Artificial Intelligence 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. |
| title | Capturing Bias Diversity in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.12839 |