Intentional Biases in LLM Responses
Nicklaus Badyal Derek Jacoby Yvonne Coady
In this study we intentionally introduce biases into large language model
responses in an attempt to create specific personas for interactive media
purposes. We explore the differences between open source models such as
Falcon-7b and the GPT-4 model from Open AI, and we quantify some differences in
responses afforded by the two systems. We find that the guardrails in the GPT-4
mixture of experts models with a supervisor, while useful in assuring AI
alignment in general, are detrimental in trying to construct personas with a
variety of uncommon viewpoints. This study aims to set the groundwork for
future exploration in intentional biases of large language models such that
these practices can be applied in the creative field, and new forms of media.