FORK : A Bite-Sized Test Set for Probing Culinary Cultural Biases in Commonsense Reasoning Models (2023)

Abstract: It is common sense that one should prefer to eat a salad with a fork rather than with a chainsaw. However, for eating a bowl of rice, the choice between a fork and a pair of chopsticks is culturally relative. We introduce FORK, a small, manually-curated set of CommonsenseQA-style questions for probing cultural biases and assumptions present in commonsense reasoning systems, with a specific focus on food-related customs. We test several CommonsenseQA systems on FORK, and while we see high performance on questions about the US culture, the poor performance of these systems on questions about non-US cultures highlights systematic cultural assumptions aligned with US over non-US cultures.

## introduction effective communication in natural language requires a shared base of knowledge between interlocutors. while this shared knowledge between communicators may be specific to individuals in a shared situation (e.g., dhruv and mei know they are sitting at a table in a restaurant), or to individuals with specialized knowledge (they are discussing backpropagation), some types of knowledge are sufficiently generic to be shared by most people in the world (e.g., objects fall when they are dropped). this latter category of commonsense knowledge has for decades been a holy grail of research in artificial intelligence and natural language understanding @xcite . if machines are to understand (and produce) human language competently, they must at a bare minimum share this commonsense knowledge with humans. a question elided by this notion of commonsense knowledge is who counts as "most people"? what may appear as universal "common sense" to ai researchers in one cultural context may in fact not be so universal. early efforts to schematize commonsense knowledge as scripts, or stereotyped a1: before the main dish. [united states] a2: after the main dish. @xcite q1: while eating, when does one drink soup? more recent ai research on commonsense knowledge acquisition has relied on crowd sourcing @xcite , corpus statistics @xcite , and language modeling @xcite in place of expertcrafted knowledge. however, each of these methods carries the potential to encode cultural bias into data and models for commonsense reasoning, whether through the implicit cultural perspectives of corpus texts, crowd source workers, or ai researchers themselves. in this work, we seek to investigate cultural biases or assumptions present in commonsense reasoning systems. culture, like commonsense knowledge, is vast. by one definition, foot_0 culture "encompasses the social behaviour and norms found in human societies, as well as the knowledge, beliefs, arts, laws, customs, capabilities, and habits of the individuals in these groups." from the social sciences, @xcite defines culture as encom-passing both material as well as non-material aspects, such as beliefs and linguistic practices. to limit the scope of our investigation, however, we focus on a single topic common to all human cultures but widely varying across them: food. we introduce fork (food oriented cultural commonsense knowledge), a manually-curated set of commonsenseqa-style @xcite test questions for probing culinary cultural biases and assumptions present in commonsense reasoning systems. for the purpose of this work, we say that a commonsense question-answering system is culturally biased if (1) in response to questions with culturally-dependent answers, it exhibits systematic preference for answers consistent with one cultural setting over others; or (2) for questions with explicit cultural contexts, it exhibits systematically higher accuracy for some cultural contexts over others. figure 1 contains an example of three interrelated test questions in fork we use to detect cultural bias. for q1, a model that prefers a1 to a2 exhibits cultural bias in favor of the united states (us) over china. while there exists no tidy mapping between human cultures and countries, in this work, we use countries as a coarse-grained proxy for culture (see: § 7). fork contains questions pertaining to the food and culinary cultures of the us, china, japan, and india with questions spanning topics of restaurant tipping, eating utensils, and other culinary customs. we test multiple encoder-based commonsenseqa models on fork, demonstrating systematic cultural biases favoring the us over non-us countries. to summarize, our contributions are: 1. fork: a "bite-sized" manually curated test set of 184 commonsenseqa-style questions which can be used for probing culinary cultural biases and assumptions in commonsense reasoning systems. 2. a systematic evaluation of several encoder based models on fork to demonstrate systematic cultural assumptions aligned with us over non-us cultures. ## dataset since fork aims to test the culinary cultural specificity of commonsense reasoning models, we choose the format to be along the lines of commonsense qa @xcite . each question in fork has two options, only one of which is cor-rect. one of the options pertains to the us culture, while the other to non-us. the questions are manually written by the first author of this paper. the source of content used to formulate the questions is information gathered from google searches, blog posts, traveler guides, etc. upon publication, we will release fork publicly. there are three types of questions in fork: the questions in fork can either be a single sentence or a two sentence question. the questions consisting of two sentences help to provide context in case of implicit and explicit questions. we also follow a template-based approach where a question about the same theme is asked multiple times, varying only by e.g. the name of a dish, city, country, etc. in total, fork consists of 184 manually curated questions, with 91.84% of questions pertaining to china, japan, india and the us, and a small number of additional questions for other countries. researching and writing questions was a slow manual process, so we chose to focus on producing more questions for fewer countries, to yield more robust results. ## experiments we summarize our experimental set up, models, and the evaluation strategy used in this work. ## results and discussion figure 2a shows the percentage of time when a us answer is chosen over a non-us answer for underspecified questions. we observe that roberta-large, and bert-large report the top two percentages of choosing a us answer over a non-us answer with values of 90.32% and 83.87% respectively. fig 2a shows that all models, except for distilbert, preferred us answers over non-us answers for a majority of underspecified questions. figure 2b shows the percentage accuracy for us and non-us implicit questions. we observe that roberta-large and bert-large report the top two accuracies of 78.57% and 71.42% respectively, when answering us implicit questions. in contrast, for non-us implicit questions, only two models, distilbert and bert-base cross the 50% accuracy mark, with bert-large having the lowest accuracy of 26.78%. figure 3a shows the percentage accuracy for us and non-us explicit questions. here, roberta-large, and bert-large report the top two accuracies of 100% and 92.30% respectively when answering us explicit questions. in contrast, for non-us explicit questions, roberta-base reports the best accuracy at 64.28% while roberta-large performs the worst, achieving 22.85%. figure 3b shows the percentage times a non-us answer is chosen for underspecified, non-us implicit and non-us explicit questions. for underspecified questions, only distilbert crosses the 50% mark, with 51.62% accuracy. the performance for non-us implicit and non-us explicit questions has been discussed above. we report all the model accuracies on fork in tables 4 and 5 in appendix a.2. in addition to aggregating us versus non-us results, we break down accuracy results for china, india, and japan for implicit and explicit questions in ## related works a growing body of work aims to detect social biases in nlp models with respect to demographic attributes like gender and race @xcite @xcite @xcite . more recent is the growing attention towards cultural biases in nlp and ai technology at large. @xcite propose a framework that allows one to understand the challenges of cultural diversity in nlp applications. wikipedia has been shown to embed latent cultural biases @xcite and @xcite propose a methodology to develop culturally aware models for english, chinese and japanese using distributional perspectives on controversial topics from wikipedia across these languages. @xcite explore cultural biases, but along the rituals like birth, coming of age, marriage etc. in the us and in india. @xcite investigate cultural bias in language proficiency tests and identify items of bias against non-native english speakers. to the best of our knowledge, this is the first work to analyze cultural bias in commonsense reasoning from the angle of culinary customs. ## conclusion we have introduced fork, a dataset to measure culinary cultural bias in commonsense models. confirming our hypothesis, we find that models default to us cultural contexts in underspecified questions, and perform markedly better on implicit and explicit questions about us culture than non-us. a likely source of bias is the english, usproduced texts which models are pretrained on. we believe the results support our hypothesis that english language models lms trained on texts (many of which are produced for a us audience) would reflect us (or broadly western) cultural assumptions. we hypothesize that the underspecified setting had the lowest "accuracy" for non-us countries because the experimental design forced the model to choose between us and non-us interpretations of the same question. for implicit and explicit settings, we speculate that the non-us accuracy is generally higher for explicit than implicit because the former made it easier for models to determine the cultural setting. potential mitigation techniques to eliminate such biases may involve better curation of training data, training separate models for different cultural contexts, training models to better recognize cultural cues or ask for clarification in ambiguous settings, among many other possibilities. we believe this is an open research question, and we hope this paper will inspire future research to address it. the topic of cultural bias is vast, and we choose a narrow scope to avoid biting off more than we can chew. future work will explore strategies for cultural awareness of commonsense models, analysis of cultural assumptions in non-english models, and analysis of other aspects of culture beyond the culinary.