StereoWipe is a research project dedicated to creating comprehensive benchmarks for evaluating bias in Large Language Models, with a focus on subjective cultural assessments.
We are developing benchmarks to measure bias in Large Language Models, with a focus on subjective cultural assessments.
We create large-scale datasets of prompts and responses to evaluate bias across a wide range of cultural contexts. Our first benchmark is BiasWipe.
We use state-of-the-art language models to assess bias with nuanced understanding, and we study the effectiveness of this approach.
Our work focuses on the challenges of evaluating bias in a global context, and we are developing new methods for subjective cultural assessments.
A high-level overview of our research methodology for the BiasWipe benchmark.
We collect a diverse set of prompts and responses from a variety of sources, including open-ended generation and human-written examples.
We work with a team of annotators from around the world to label our data for a wide range of biases.
We use our benchmark to evaluate a variety of Large Language Models, and we publish our results to the community.
We are developing new methods for mitigating bias in LLMs, and we are working with the community to make these methods available to everyone.
StereoWipe addresses a critical gap in AI evaluation. While current benchmarks often rely on abstract definitions and Western-centric assumptions, we provide a nuanced, globally-aware approach to measuring bias in language models.
Our benchmark empowers developers, researchers, and policymakers to build AI systems that serve all communities equitably, promoting social understanding rather than reinforcing harmful biases.