Introducing MARB — A Dataset for Studying the Social Dimensions of Reporting Bias in Language Models (2025)

Abstract: Reporting bias is the tendency for speakers to omit unnecessary or obvious information while mentioning things they consider relevant or surprising. In descriptions of people, reporting bias can manifest as a tendency to over report on attributes that deviate from the norm. While social bias in language models has garnered a lot of attention in recent years, a majority of the existing work equates “bias” with “stereotypes”. We suggest reporting bias as an alternative lens through which to study how social attitudes manifest in language models. We present the MARB dataset, a diagnostic dataset for studying the interaction between social bias and reporting bias in language models. We use MARB to evaluate the off-the-shelf behavior of both masked and autoregressive language models and find signs of reporting bias with regards to marginalized identities, mirroring that which can be found in human text. This effect is particularly pronounced when taking gender into account, demonstrating the importance of considering intersectionality when studying social phenomena like biases.

## introduction the issue of social bias in language models has received increased attention in the past few years, with many recent efforts focusing on benchmark datasets for quantifying bias in a way that is comparable across models @xcite . the majority of work in this area equates "bias" with "stereotypes" @xcite . while stereotypes are indeed one way in which social inequalities manifest in language, they are only one of the symptoms of a larger underlying problem. language in itself is a social phenomenon @xcite . utterances do not only communicate semantic and pragmatic content; they also mirror the social perspective of the speaker. in order to better predict potential harms caused by language models, we need a more holistic un-derstanding of "bias" that connects model behavior with social norms, attitudes and expectations. in other words, we do not see bias as inherently or necessarily bad. instead, we view biases as symptoms of a perspective being encoded in the model. we suggest reporting bias, or "the tendency of people to not state the obvious" @xcite , as a lens through which to study social norms and attitudes in language models. in descriptions of people, reporting bias can manifest as a tendency to over report on attributes that deviate from the norm, drawing further attention to the perceived otherness (see e.g. thomas-olalde and velho, 2011) of already marginalized groups. despite the obvious connection, the relationship between reporting bias and social biases has not previously been studied. to address this research gap, we introduce the marked attribute and reporting bias dataset, or marb for short, for measuring model reporting bias with regards to sensitive human attributes such as race, queerness and disability. we generate templates from naturally occurring english text, which are then populated with different descriptors related to these attributes. the full dataset and usage instructions can be found on github. 1 we introduce the dataset in section 4 and discuss the theoretical motivations and technical implementation behind it, as well as recommendations for how it can be used. as an example of this recommended usage, we evaluate six popular large language models with marb in section 5. we find signs of reporting bias with regards to marginalized attributes, similar to that which is found in online news media. we also find that the gender of the person being described has a noticeable effect on the observed reporting bias, in that sentences describing women are generally more likely to mention attributes like race or queerness. the effect is particularly striking for sen-tences mentioning asian women, underlining the importance of taking intersectionality foot_1 into account when studying bias. ## related work reporting bias in training data has been shown to affect the commonsense knowledge acquired by language models @xcite . much of the existing work in this area focuses on visual commonsense knowledge, such as the colors of common objects @xcite @xcite . the issue of social biases in language models has received increasing attention in recent years @xcite . the majority of works in this field have focused specifically on gender and/or racial bias in simple binary settings such as male/female, white/black (e.g. kiritchenko and mohammad, 2018; @xcite . however, more recent work has also branched out to finer-grained analyses of biases against other social groups, such as people with disabilities @xcite and queer people @xcite . @xcite note the need to consider intersectional biases, an area that is still under-researched. a growing body of research has been directed towards quantifying social biases in ways that are generalizable across models. many of these benchmarks and diagnostic datasets rely on artificially constructed templates (e.g. @xcite or crowdworkers (e.g. @xcite for contrasting examples. the majority of these papers conceptualize "bias" as stereotypes. there has not been any previous work studying the interactions between reporting bias and social biases. ## reporting bias and markedness human language is underspecified. when we talk, we leave out the things we consider unimportant, inferrable from context or simply too obvious to mention. this behavior, described by @xcite as the maxim of quantity, leads to a discrepancy between reality and description that is known as reporting bias. @xcite builds on gricean theory by considering what makes something too obvious to mention. he suggests that linguistic expressions have so-called default interpretations: when we hear a certain expression, the interpretation closest at hand will often be the most typical or normative one. if we want to describe a situation that differs from that norm, we need to specify by marking it in our message. thus, in human communication, "what is simply described is stereotypically exemplified" @xcite , while a marked message indicates a marked situation. to use a frequent example from previous work on reporting bias (e.g. @xcite , while most of us would agree that bananas are typically yellow, the bigram "green banana" tends to be more frequent than "yellow banana" in text. figure 1 gives an example of how the same phenomenon manifests in descriptions of people. the girl in 1a is simply described as "a little girl", while the girl in 1b is described as "an asian girl". we can interpret this as the annotator considering "white" to be the default for little girls, and thus too obvious to mention in the caption. 3 in in practice unmarked messages tend to be more common than marked messages regardless of the attribute in question. it would be inefficient to include every single detail when describing a situation. on the other hand, we would expect marked messages to be more common for marked attributes than for unmarked ones, in accordance with the gricean maxim of quantity. ## experimental setup we present two case studies in this section to illustrate the kind of analyses that are possible using the marb dataset. in both studies, we measure the effect of adding the attribute descriptors by comparing marked sequences (those mentioning the attribute) to the corresponding unmarked template sequences. we focus on one category per case study in order to simplify analyses and to better showcase what can be done with the marb dataset. moreover, it reduces the environmental impact of our experiments. the first experiment uses the race category to study masked language models. the second experiment uses the queerness category to study auto-regressive models. ## conclusion in this paper we explore how reporting bias with regards to marked and marginalized identities manifests in language models. to that end, we create the marb dataset: a diagnostic dataset meant to study the intersection between social bias and reporting bias via marked and unmarked attributes. we use marb to evaluate the out-of-the-box behavior of six popular language models, and find that they show signs of reporting bias with regards to marked attributes, mirroring that found in text corpora. particularly noticeable are the intersectional effects of gender in combination with other attributes, showing that sensitive attributes like race and queerness are more likely to be mentioned in descriptions of women. our results demonstrate that there is a strong connection between reporting bias and social norms and attitudes, recommending reporting bias as a promising direction for future research on social bias in language models. as a way of quantifying social norms through language, the framework and methods presented in this paper could also provide new tools for fields like linguistics and social science. we encourage future work to continue investigating the ways in which social norms manifest in language through reporting bias using the framework presented here, and to extend the marb dataset to cover more categories and attribute descriptors.