What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909791474941952 |
|---|---|
| author | Pan, Jinhao Raj, Chahat Yao, Ziyu Zhu, Ziwei |
| author_facet | Pan, Jinhao Raj, Chahat Yao, Ziyu Zhu, Ziwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs Pan, Jinhao Raj, Chahat Yao, Ziyu Zhu, Ziwei Computation and Language 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. |
| title | What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.19749 |