As language models (LMs) become increasingly powerful, it is important to
quantify and compare them for sociodemographic bias with potential for harm.
Prior bias measurement datasets are sensitive to perturbations in their
manually designed templates, therefore unreliable. To achieve reliability, we
introduce the Comprehensive Assessment of Language Model bias (CALM), a
benchmark dataset to quantify bias in LMs across three tasks. We integrate 16
existing datasets across different domains, such as Wikipedia and news
articles, to filter 224 templates from which we construct a dataset of 78,400
examples. We compare the diversity of CALM with prior datasets on metrics such
as average semantic similarity, and variation in template length, and test the
sensitivity to small perturbations. We show that our dataset is more diverse
and reliable than previous datasets, thus better capture the breadth of
linguistic variation required to reliably evaluate model bias. We evaluate 20
large language models including six prominent families of LMs such as Llama-2.
In two LM series, OPT and Bloom, we found that larger parameter models are more
biased than lower parameter models. We found the T0 series of models to be the
least biased. Furthermore, we noticed a tradeoff between gender and racial bias
with increasing model size in some model series. The code is available at
this https URL

评估语言模型偏见的全面评估基准数据集（CALM）是用于量化与比较语言模型社会人口统计偏见的重要资源，通过整合现有数据集并构建包含 78,400 个样例的 244 个模板的数据集，CALM 数据集更具多样性和可靠性，能更好地评估语言模型的广度和偏见。

CALM：一个综合评估语言模型偏见的多任务基准

CALM : A Multi-task Benchmark for Comprehensive Assessment of Language  Model Bias

Deep neural networks often learn unintended biases during training, which
might have harmful effects when deployed in real-world settings. This paper
surveys 209 papers on bias in NLP models, most of which address
sociodemographic bias. To better understand the distinction between bias and
real-world harm, we turn to ideas from psychology and behavioral economics to
propose a definition for sociodemographic bias. We identify three main
categories of NLP bias research: types of bias, quantifying bias, and
debiasing. We conclude that current approaches on quantifying bias face
reliability issues, that many of the bias metrics do not relate to real-world
biases, and that current debiasing techniques are superficial and hide bias
rather than removing it. Finally, we provide recommendations for future work.

本篇论文调查了 209 篇关于自然语言处理模型中的偏差的论文，提出了社会人口统计学偏见的定义，并确定了研究偏见的三个主要类别：偏见类型，偏见度量和去偏见。作者总结说，目前的去偏见技术是肤浅的，不能真正消除偏见；最后提供了未来工作的建议。