Social Bias in Popular Question-Answering Benchmarks

Question-answering (QA) and reading comprehension (RC) benchmarks are essential for assessing the capabilities of large language models (LLMs) in retrieving and reproducing knowledge. However, we demonstrate that popular QA and RC benchmarks are biased and do not cover questions about different demographics or regions in a representative way, potentially due to a lack of diversity of those involved in their creation. We perform a qualitative content analysis of 30 benchmark papers and a quantitative analysis of 20 respective benchmark datasets to learn (1) who is involved in the benchmark creation, (2) how social bias is addressed or prevented, and (3) whether the demographics of the creators and annotators correspond to particular biases in the content. Most analyzed benchmark papers provided insufficient information regarding the stakeholders involved in benchmark creation, particularly the annotators. Notably, just one of the benchmark papers explicitly reported measures taken to address social representation issues. Moreover, the data analysis revealed gender, religion, and geographic biases across a wide range of encyclopedic, commonsense, and scholarly benchmarks. More transparent and bias-aware QA and RC benchmark creation practices are needed to facilitate better scrutiny and incentivize the development of fairer LLMs.

Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research

With language technology increasingly affecting individuals' lives, many recent works have investigated the ethical aspects of NLP. Among other topics, researchers focused on the notion of morality, investigating, for example, which moral judgements language models make. However, there has been little to no discussion of the terminology and the theories underpinning those efforts and their implications. This lack is highly problematic, as it hides the works' underlying assumptions and hinders a thorough and targeted scientific debate of morality in NLP. In this work, we address this research gap by (a) providing an overview of some important ethical concepts stemming from philosophy and (b) systematically surveying the existing literature on moral NLP w.r.t. their philosophical foundation, terminology, and data basis. For instance, we analyse what ethical theory an approach is based on, how this decision is justified, and what implications it entails. Our findings surveying 92 papers show that, for instance, most papers neither provide a clear definition of the terms they use nor adhere to definitions from philosophy. Finally, (c) we give three recommendations for future research in the field. We hope our work will lead to a more informed, careful, and sound discussion of morality in language technology.