Social Bias Benchmark for Generation: A Comparison of Generation and QA -Based Evaluations (2025)

Abstract: Measuring social bias in large language models (LLMs) is crucial, but existing bias evaluation methods struggle to assess bias in long-form 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 multiple-choice BBQ evaluation, showing that the two approaches produce inconsistent results.

## introduction despite significant progress in recent years, large language models (llms) still reflect and reinforce social biases embedded in their training datasets, resulting in unfair and discriminatory outcomes for marginalized groups @xcite @xcite . addressing these biases requires accurately quantifying them, but the existing evaluations often rely on multiplechoice question formats @xcite @xcite @xcite , inter alia), which do not fully capture the nuanced and context-dependent nature of natural language generation @xcite . to evaluate the social bias of llms in long-form generation, we propose the bias benchmark for generation (bbg), a benchmark for assessing bias in story generation, built on the english bbq @xcite and korean bbq (kobbq) @xcite datasets. to adapt the existing multiplechoice format of bbq for long-form generation, we first obfuscate contextual data by replacing character descriptions with neutral placeholders and ## related work social bias in generation of language models has been evaluated using lexicon-based methods @xcite , finetuned models @xcite @xcite @xcite , llm inferences @xcite @xcite , and human evaluations @xcite . bias assessments in multiplechoice qa (mcq) formats @xcite @xcite @xcite are widely used in llm evaluation @xcite @xcite , as they allow measuring social bias in downstream tasks without relying on external models. however, recent critiques on mcq-based evaluations @xcite @xcite highlight the need for a comparative analysis with long-form generation evaluations. we propose a benchmark 1 our bbg dataset and evaluation code are available at https://jinjh0123.github.io/bbg . though they were both doing fairly well early in the semester, a man and a woman each dropped out of grad school at the end of the semester. one mentioned plans to get married and adopt children soon as the primary reason for leaving, while the other mentioned a new opportunity that came up working for an uncle who lives out of state. continue the following story. for measuring social bias in downstream story generation, covering 9 categories in english and 12 in korean, enabling direct comparison between generation and qa-based evaluations. the bias benchmark for question answering (bbq) @xcite evaluates social bias in language models through multiple-choice reading comprehension. as illustrated in figure 1 , each passage describes a scenario involving two individuals from different social groups, with a question addressing related stereotypes. the benchmark has two formats: one requiring an answer based only on an initial ambiguous context, where the correct answer is 'unknown,' and another incorporating a following disambiguating context, where the correct answer is one of the two individuals. ## conclusion we introduce a framework for evaluating social bias in generation through the bias benchmark for generation (bbg). assessing various llms on story generation and comparing it with multiplechoice qa-based evaluation, we find that llms exhibit notably different social biases between longform generation and reading comprehension qa. this study underscores the need for comprehensive bias evaluations, offering a valuable resource for developing fairer nlp systems.