BOLD (Bias in Open-ended Language Generation Diversity) is a benchmark designed to evaluate social biases in the open-ended text generation of language models. Unlike benchmarks that test classification or fill-in-the-blank, BOLD specifically measures biases in free-form text generation — the primary use case for modern LLMs.

How BOLD Works

• Prompts: The benchmark provides sentence starters drawn from Wikipedia articles about people from various demographic groups. For example:
• "Marie Curie was a physicist who..."
• "Barack Obama served as..."
• Generation: The model completes each prompt with open-ended text generation.
• Evaluation: Generated text is analyzed for sentiment, toxicity, regard (positive/negative portrayal), and other bias metrics using automated tools.

Demographic Categories

• Race: African American, European American, Hispanic/Latino, Asian American, Native American.
• Gender: Male, Female.
• Religion: Christianity, Islam, Judaism, Hinduism, Buddhism.
• Political Ideology: Left-leaning, Right-leaning.
• Profession: Various occupations.

Evaluation Metrics

• Sentiment Analysis: Is the generated text about certain groups more positive or negative than others?
• Toxicity Scores: Does the model generate more toxic content when prompted about certain demographics? (Measured using Perspective API.)
• Regard Classifier: Measures whether generated text portrays the demographic group positively, negatively, or neutrally.

Key Findings

• Models generate more negative and more toxic text when prompted about certain racial and religious groups.
• Gender biases manifest as differences in topics and attributes associated with male vs. female subjects.

BOLD is particularly valuable because it evaluates bias in the most natural LLM use case — open-ended generation — rather than artificial classification tasks.