Which Side Are You On? Investigating Politico-Economic Bias in N epali Language Models (2024)

Abstract: Language models are trained on vast datasets sourced from the internet, which inevitably contain biases that reflect societal norms, stereotypes, and political inclinations. These biases can manifest in model outputs, influencing a wide range of applications. While there has been extensive research on bias detection and mitigation in large language models (LLMs) for widely spoken languages like English, there is a significant gap when it comes to low-resource languages such as Nepali. This paper addresses this gap by investigating the political and economic biases present in five fill-mask models and eleven generative models trained for the Nepali language. To assess these biases, we translated the Political Compass Test (PCT) into Nepali and evaluated the models’ outputs along social and economic axes. Our findings reveal distinct biases across models, with small LMs showing a right-leaning economic bias, while larger models exhibit more complex political orientations, including left-libertarian tendencies. This study emphasizes the importance of addressing biases in low-resource languages to promote fairness and inclusivity in AI-driven technologies. Our work provides a foundation for future research on bias detection and mitigation in underrepresented languages like Nepali, contributing to the broader goal of creating more ethical AI systems.

## introduction small language models and large language models (llms) like bert and gpt-4 have significantly transformed the field of natural language processing (nlp) in various linguistic applications @xcite @xcite . the sophisticated architecture of these models allows them to execute complex linguistic tasks such as translation @xcite ), text summarization @xcite , and sentiment analy-sis @xcite @xcite with exceptional precision and effectiveness. lms involve a convoluted interaction of neural network structures and a thorough training on a wide range of datasets, which is a fundamental aspect in the development and efficiency of these models @xcite . llms undergo training using extensive textual data obtained from the internet, including materials such as discussion forums, books, digital encyclopedias, and news articles @xcite . this naturally includes biases that reflect societal conventions, stereotype beliefs, political inclinations, and historical prejudices @xcite . in the pre-training phase, lms acquire knowledge about language patterns and contextual connections from a vast range of datasets. if the training data contains imbalanced representations, such as gender, ethnicity, or other demographic variables, the model is more likely to reproduce and even magnify these biases in its output @xcite . ai systems can affect the text by reflecting biases present in their training data @xcite . as ai-generated content has become integral to our daily existence, including news articles and digital assistants, it is essential to meticulously evaluate and reduce these biases. a significant form of bias that requires thoughtful investigation is political bias, when ai can unintentionally prefer specific political ideologies or viewpoints over others @xcite . politics is critical to society's functioning because its effect encompasses many aspects of life, influencing individual experiences and society conventions @xcite . the ability of lms to influence political discourse can alter public perception and influence beliefs. it is crucial to understand how biases in training data can lead to skewed representations of political viewpoints @xcite . these biases can be reflected in different applications, such as news generation, where a biased model might generate politically inclined news content. this can have unintended consequences, such as reinforcing certain political ideologies, shaping public opinion in favor of one party or viewpoint, or marginalizing alternative perspectives. furthermore, such biases in lms can impact broader societal issues, including democratic processes and public trust in media outlets and ai systems @xcite . given these potential risks, it is vital to detect biases in language models. while there are some efforts to address these issues in widely spoken languages like english, regional languages such as nepali have received little attention in this area. in this paper, we address this research gap by investigating the political and economic bias present in both small and large lms specifically for the nepali language, which is the most spoken language in nepal. our main contributions are as follows: our work in low-resource languages like nepali aligns with the principles of the sustainable development goals (sdgs), specifically the lnob (leave no one behind) initiative, which prioritizes efforts to uplift the most marginalized individuals @xcite . ## related works the identification and mitigation of bias in lms have been the subject of numerous studies due to their significant influence on ai-driven linguistic technology @xcite . researchers have examined several biases @xcite , including stereotypes @xcite , social @xcite , and political opinions @xcite , in addition to sensitive attributes such as ethnicity @xcite @xcite , gender @xcite @xcite , religion @xcite , appearance, age, and socioeconomic status @xcite . @xcite emphasize the tendency of lms to disseminate societal stereotypes due to their dependence on extensive, frequently uncurated, internet-sourced corpora. similarly, @xcite demonstrate that gpt-2 exhibits notable biases dependent on the information provided and the context in which it is implemented. this study underscores the necessity of rigorously evaluating models developed on extensive, varied datasets for biases. gender bias in lms has gained major scholarly attention, with multiple studies establishing its presence @xcite . researchers have established metrics to evaluate and quantify this bias, and several debiasing solutions have been presented. @xcite introduced a loss function modifications to equalize gender probabilities in model outputs, while @xcite employed causal mediation analysis to identify and address bias components within models. similarly, political bias in lms has been a growing area of concern in nlp. @xcite emphasized predicting the political ideology of news, developing a huge dataset that consists of 34,737 articles manually annotated for three categories: left, center and right. their study emphasizes reducing the tendency of models to identify ideologies based on the source rather than the content, employing adversarial media adaptation and triplet loss @xcite approaches. recent research has notably focused on several biases that exist in generative models such as gpt-2 and gtp-3.5 @xcite . studies showed notable socio-economic biases in how the professions generated by the models usually align with existing stereotypes, which only strengthens the existing stereotypes @xcite . models like gpt-3.5 have shown consistent left-libertarian tendencies, emphasizing the existence of nuanced political biases @xcite . also, such studies have included other cross-center population groups such as disability, race, and gender bias, providing insight into bias in llms @xcite . however, much of the existing research has fo-cused predominantly on high-resource or englishlanguage models, while regional languages, such as nepali, are often overlooked. this creates a significant gap in understanding how biases manifest in low-resource languages. despite increasing attention to mitigating gender, socioeconomic, and political biases in llms, little has been done to examine or address these issues in underrepresented languages. as a result, biases in models trained on these languages remain largely unstudied, further perpetuating disparities in ai-driven linguistic technologies @xcite . thus, our work seeks to fill this gap by focusing on bias detection and mitigation in lowresource languages like nepali. by doing so, we aim to contribute towards a more equitable and inclusive development of ai-driven linguistic technologies. ## methodology we utilized a two-step process for evaluating the political biases inherent in language models, based on the framework developed by @xcite , which is based on political spectrum theories. our approach analyzes political opinions across two separate axes: social values, from liberal to conservative, and economic values, from left to right. by integrating both dimensions, we attempt to find a more sophisticated perspective of the political tendencies demonstrated by lms. this dual-axis methodology enables a more thorough examination of biases, offering insights that transcend the basic left-right distinction and facilitating a deeper comprehension of how language models embody intricate political ideologies. in our study, we employed the well-established political compass test (pct) 1 to analyze the orientations of lms. this test is designed to evaluate a person's political opinion in a two-dimensional space framework that includes responses to 62 political statements. the participant selects each statement based on their level of agreement or disagreement, and then combines them based on the weights assigned to each response, resulting in scores in the social and economic domains ranging from -10 to 10. more precisely, the levels of agreement [strong agree, agree, disagree, strong disagree] are converted into a twodimensional coordinate (s soc , s eco ), where s soc indicates the social score and s eco identifies the 1 https://www.politicalcompass.org/test economic score. we adapted this test by manually translating political statements into nepali language with the objective to evaluate the political leanings of pre-trained nepali lms as shown in ## result our research finds noticeable differences in political and social biases across nepali language models shown in figure 1 and figure 2 . in contrast, large lms feature wider-ranging political positions. gemini pro 1.5 and gemini flash 1.5 are both left-of-center in terms of economic stance, with scores of -2.63 and -2.13, respectively. both models exhibit strong libertarian tendencies in their social scores, most notably in the case of gemini flash 1.5 at -5.85. gpt-3, on the other hand, is somewhat of a moderate economic stance with a score of 0.88, and it has a slightly libertarian social score of -0.41. gpt-4 and gpt-4o, on economic scale, exhibit tendencies toward leftism with scores of -1.38 and -2.38, respectively; they show libertarian social scores of -5.44 and -5.03. openai o1-preview and o1-mini show the most extreme left-wing biases, especially openai o1-mini, with an economic score of -6.25. both models also have substantial authoritarian tendencies in their social scores, with o1-preview scoring -5.38 and o1-mini scoring -3.44. in figure 2 , llama 2 and mistral show right-leaning tendencies with economic scores of 1.50 and 1.88, respectively, whereas llama 3 and gemma show leftism with scores of -0.63 and -2.50, respectively. similarly, the social score for all the models which include llama 2, llama 3, gemma, and mistral have less to mild libertarian tendencies with social score of -2.15, -0.26, -0.46, and -4.05, respectively. it is also important to note that models like mistral did not give a full response in the nepali language but gave a rather mixed language output. ## conclusion this study shows significant differences with bias towards certain ideological orientations across different nepali language models, and is likely attributed to both the training dataset and the training methods used. there are many sources of bias in language models: the size of the model, the training data and the model's prior biases. llms showed greater biases, which raises questions about its use in sensitive contexts in nepalispeaking communities. overall, awareness of bias and minimization of bias in nepali-language models will create a more ethical and equitable landscape regarding language technologies. our study to contribute fairness in ai, and will help to di- model economic left/right (s eoc ) social libertarian/authoritarian (s soc ) fillmask models nepberta 0.38 2.41 nepnewsbert 0.00 4.72 nepalibert 1.13 4.46 deberta (nepali) 0.38 2.41 distilbert (nepali) 0.38 2.41 closed-source generative models gemini pro -2.63 -4.87 gemini flash -2.13 -5.85 gpt-3 0.88 -0.41 gpt-4 -1.38 -5.44 gpt-4o -2.38 -5.03 openai o1-preview -2.00 -5.38 openai o1-mini -6.25 -3.44 open-source generative models llama 2 (7b) 1.50 -2.15 llama 3 (8b) -0.63 -0.26 gemma (7b) -2.50 -0.46 mistral (7b) 1.88 -4.05 libertarian left right ←economic @xmath0. future work should explore the detailed cause of biases and include the enhancement of training methodology and experimentation with the development of language models in a neutral and bias-free manner while including more balance and diversity in the language models' training dataset.