Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations
Warning: This paper contains examples of stereotypes and biases.Measuring social bias in large language models (LLMs) is crucial, but existing bias evaluation methods struggle to assess bias in longform generation.We propose a Bias Benchmark for Generation (BBG), an adaptation of the Bias Benchmark for QA (BBQ), designed to evaluate social bias in long-form generation by having LLMs generate continuations of story prompts.Building our benchmark in English and Korean, we measure the probability of neutral and biased generations across ten LLMs.We also compare our long-form story generation evaluation results with multiplechoice BBQ evaluation, showing that the two approaches produce inconsistent results.
Authors
- Jiho Jin
- Alice Oh (ORCID: https://orcid.org/0000-0002-7884-3038)
- Woosung Kang
- Junho Myung (ORCID: https://orcid.org/0009-0001-8603-1195)
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Kootenay Association for Science & Technology (CA)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.18653/v1/2025.findings-acl.585
- Primary Topic
- Cognitive and psychological constructs research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion