A Benchmark for Subjective Cultural Assessments of LLMs

StereoWipe is a research project dedicated to creating comprehensive benchmarks for evaluating bias in Large Language Models, with a focus on subjective cultural assessments.

Our Research

We are developing benchmarks to measure bias in Large Language Models, with a focus on subjective cultural assessments.

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Benchmark Development

We create large-scale datasets of prompts and responses to evaluate bias across a wide range of cultural contexts. Our first benchmark is BiasWipe.

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LLM-as-a-Judge

We use state-of-the-art language models to assess bias with nuanced understanding, and we study the effectiveness of this approach.

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Cultural Assessments

Our work focuses on the challenges of evaluating bias in a global context, and we are developing new methods for subjective cultural assessments.

Our Methodology

A high-level overview of our research methodology for the BiasWipe benchmark.

1

Data Collection

We collect a diverse set of prompts and responses from a variety of sources, including open-ended generation and human-written examples.

2

Human Annotation

We work with a team of annotators from around the world to label our data for a wide range of biases.

3

Model Evaluation

We use our benchmark to evaluate a variety of Large Language Models, and we publish our results to the community.

4

Bias Mitigation

We are developing new methods for mitigating bias in LLMs, and we are working with the community to make these methods available to everyone.

About StereoWipe

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.