OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
Patrick Haller Ansar Aynetdinov Alan Akbik
Instruction-tuned Large Language Models (LLMs) have recently showcased
remarkable ability to generate fitting responses to natural language
instructions. However, an open research question concerns the inherent biases
of trained models and their responses. For instance, if the data used to tune
an LLM is dominantly written by persons with a specific political bias, we
might expect generated answers to share this bias. Current research work seeks
to de-bias such models, or suppress potentially biased answers. With this
demonstration, we take a different view on biases in instruction-tuning: Rather
than aiming to suppress them, we aim to make them explicit and transparent. To
this end, we present OpinionGPT, a web demo in which users can ask questions
and select all biases they wish to investigate. The demo will answer this
question using a model fine-tuned on text representing each of the selected
biases, allowing side-by-side comparison. To train the underlying model, we
identified 11 different biases (political, geographic, gender, age) and derived
an instruction-tuning corpus in which each answer was written by members of one
of these demographics. This paper presents OpinionGPT, illustrates how we
trained the bias-aware model and showcases the web application (available at
https://opiniongpt.informatik.hu-berlin.de).