What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs
This addresses the issue of measuring social bias in LLMs for AI fairness and safety, providing a more accurate evaluation framework, though it is incremental as it builds on existing bias assessment methods.
The authors tackled the problem of social bias in LLMs that persists in subtle, contextually hidden forms not captured by traditional term-based benchmarks, and introduced the Description-based Bias Benchmark (DBB) to assess bias at the semantic level, revealing that six state-of-the-art LLMs continue to reinforce biases in nuanced settings despite reducing bias at the term level.
Large Language Models (LLMs) often exhibit social biases inherited from their training data. While existing benchmarks evaluate bias by term-based mode through direct term associations between demographic terms and bias terms, LLMs have become increasingly adept at avoiding biased responses, leading to seemingly low levels of bias. However, biases persist in subtler, contextually hidden forms that traditional benchmarks fail to capture. We introduce the Description-based Bias Benchmark (DBB), a novel dataset designed to assess bias at the semantic level that bias concepts are hidden within naturalistic, subtly framed contexts in real-world scenarios rather than superficial terms. We analyze six state-of-the-art LLMs, revealing that while models reduce bias in response at the term level, they continue to reinforce biases in nuanced settings. Data, code, and results are available at https://github.com/JP-25/Description-based-Bias-Benchmark.