This Land is Your, My Land: Evaluating Geopolitical Bias in Language Models through Territorial Disputes (2024)

Abstract: Do the Spratly Islands belong to China, the Philippines, or Vietnam? A pretrained large language model (LLM) may answer differently if asked in the languages of each claimant country: Chinese, Tagalog, or Vietnamese. This contrasts with a multilingual human, who would likely answer consistently. In this paper, we show that LLMs recall certain geographical knowledge inconsistently when queried in different languages—a phenomenon we term geopolitical bias. As a targeted case study, we consider territorial disputes, an inherently controversial and multilingual task. We introduce BorderLines, a dataset of territorial disputes which covers 251 territories, each associated with a set of multiple-choice questions in the languages of each claimant country (49 languages in total). We also propose a suite of evaluation metrics to precisely quantify bias and consistency in responses across different languages. We then evaluate various multilingual LLMs on our dataset and metrics to probe their internal knowledge and use the proposed metrics to discover numerous inconsistencies in how these models respond in different languages. Finally, we explore several prompt modification strategies, aiming to either amplify or mitigate geopolitical bias, which highlights how brittle LLMs are and how they tailor their responses depending on cues from the interaction context. Our code and data are available at https://github.com/manestay/borderlines.

## introduction pretrained large language models (llms) have recently seen widespread adoption by users worldwide due to their capabilities with generative tasks ranging from drafting emails to writing code. this has given rise to the impression of llms as interactive, general-purpose knowledge bases (kbs). while this view is popular among end-users, nlp figure 1 : illustration of a disputed territory and the proposed approach, where we ask the same query in different languages. the llm responds inconsistently: in spanish and english "spain", while in arabic "morocco", demonstrating geopolitical bias. we compare the responses to kb triplet, which says the territory "ceuta" belongs to the claimant "spain". however, this has not stopped the massive adoption of these systems in society. but while llms may prove to be quite useful in purely creative tasks, their use in generating content that is faithful to real-world facts is not free of challenges. in particular, the biases they learn from their training data can lead to unexpected issues, especially when dealing with politically and socially sensitive topics. this motivates our study on how llms operate with respect to territorial disputes, wherein multiple countries claim their rightful ownership of certain territories. we proceed with the insight that these countries, neighboring or not, often speak different languages, and, in such cases, llms' training data will learn different views of the "factual" situation depending on the language. in our work, we find that this is indeed the case, as we uncover llms' propensity for geopolitical bias -a tendency to report geopolitical knowledge differently depending on the language of interaction. the existence of geopolitical bias in llms underscores a major risk, in that users with similar information-seeking queries will instead receive different "factual" information depending on the language of choice. while such adaptation of output may be a desirable quality in the context of cultural sensitivity -wherein users may want to receive responses that are tailored to their cultural norms -we argue that this may be less desirable in the context of territorial disputes (and related factual inquiries). as these systems come to be used for a wide variety of tasks, it is problematic for them to answer with different facts to different users simply because that is what it thinks they may want to hear. llms which display geopolitical bias amplify divisions in viewpoints across cultures, increasing the societal risks of using such systems. furthermore, geopolitical bias calls into question the true cross-lingual ability of llms. while multilingual humans are more likely to reconcile facts written in different languages, we show that multilingual llms store and recall facts differently. our study takes a deep dive into evaluating the research question: "do multilingual llms recall information differently when responding to the same underlying queries specified in different languages?" after quantifying the extent of geopolitical bias in several popular llms, we perform a deeper dive into how bias can be amplified or mitigated by modifying the conversational contexts. one technique we use is personas, in which we ask an llm to answer from the perspective of some individual (which the llm can associate with a certain biased or neutral geopolitical leaning). our contributions are as follows: and contexts. 3. our evaluations lead to several non-intuitive findings: instruction-tuned llms underperform base ones, larger llms can underperform smaller ones, and geopolitical bias is amplified in stronger models. 4. we further show that llms' knowledge across languages is brittle. we do so by effecting the geopolitical bias of llm responses through several well-motivated prompt modification strategies: a nationalist persona, a neutral (un peacekeeper) persona, and demographic reasoning. 5. we present case studies on three highly contentious territorial disputes, which highlight llms' language-dependent biases and inconsistencies. cultural biases of llms various studies have looked at bias in large language models from social and cultural perspectives. cao et al. (2023) probe chatgpt's cross-cultural alignment to different cultures by prompt the llm with culturally-sensitive prompts in multiple languages. they find chatgpt favors american cultures, especially when interacting with the llms in english, and other languages interactions are not as culturally specific. naous et al. (2023) perform a deep dive into llms' understanding of western vs arab world values, with similar findings. cheng et al. (2023) explore social biases through asking llms to generate personas, which are natural language descriptions of certain demographic groups. they find that generated personas amplify stereotypes against marginalized groups. our work also has experiments asking llms to adopt personas. abid et al. (2021) highlights the potential danger of llms, which can associate certain minorities with violence. they propose to alleviate some issues through prompt-based debiasing strategies, which are also considered in our work. arora et al. (2023) modify well-established cultural survey questions to serve as prompts for probing these biases, showing that these biases only weakly correlate with the value surveys themselves. @xcite trace these issues back to the training data, finding that llms reinforce biases in the text that they are trained on. by probing llms with statements from the political compass test, they quantify the political stance of the models and show that they exhibit marked differences in ideological leanings, especially on social issues. in the same vein, @xcite find that when pretraining an llm is not possible, prompting them to respond as if they were members of a particular culture can reduce this bias to some degree. concurrent work to ours shows that such language-specific biases even extend to facts. @xcite study factual consistency in multilingual llms by probing them with factual statements and find that consistency remains low across model families and sizes, and is markedly higher between european languages and those written in the latin script. this is in line with the trends observed in the socio-cultural analyses discussed above, where models are found to be more biased towards western cultures and values. this is problematic and stands in the way of reliable adoption of llms in other parts of the world. zooming out, @xcite explore the "sychophantic" behavior of llms -when llms repeat back a dialog user's preferred answer. their main consideration is in political discourse, in which a llm will adopt a liberal viewpoint with a liberal-leaning user, and similarly if conservative. our study of how geopolitical bias manifests in territorial disputes is demonstrative of this behavior (and similarly undesirable in allow for llms to facilitate echo chambers). in this case, the different languages of interaction cues the llm to take a particular geopolitical perspective on a dispute. @xcite evaluate how llms encode geographic proximity differently in different languages. they also mention "geopolitical bias", and explore a different facet of the phenomenon than in our work. specifically, they find that llms, when generating text in different languages, overrepresent the top 10 most geopolitically important countries instead of the countries more relevant to those language's speakers. @xcite study how commonsense knowledge differs across geo-diverse cultures. their dataset covers 5 languages; an example query is "in traditional [country] wedddings, the color of the wedding dress is usually [y]". entities are inserted into this query template, which is translated into different languages that necessitate different responses. on their task, they arrive at several notable findings, which are also corroborated in our work; for example, that larger llms can underperform, and that llms are not intrinsically biased towards the west. for cultural commonsense, a person of one culture can agree that another culture has different behaviors than theirs. however, this cannot be the case for territorial disputes, where differing claims necessarily invalidate others. @xcite , in their diagnostic analysis of chatgpt's ethics on several dimensions, study a single territorial dispute to find the model's language-dependent bias. our concurrent work thoroughly fleshes out the study of territorial disputes with llms, covering many more disputes (251) and languages (49), and includes both qualitative and quantitative analysis. ## dataset collection the borderlines dataset consists of two parts: a table of disputed territories, and associated probing questions. this section discusses the table, while the following sections covers the questions. the original data source is an english wikipedia ar- ## task design we now discuss the development of the border-lines queries. given a disputed territory, we formulate a multilingual query set (mqs) -a set of multiple-choice questions, one for each language of the claimants. we leverage this as a probing task for llms, allowing us to uncover how a single model recalls facts differently across languages, given the same underlying query. the approach is illustrated in figure 1 . we begin with a single english query, a multiplechoice question of the following format: is t a territory of l 1 ) c 1 or l 2 ) c 2 . . .? there can be @xmath0. 5 the letters l indicate that this is a multiple-choice question -a common task used to train and evaluate llms. borderlines task after the translation process, each territorial dispute includes a multilingual query set (mqs), which are queries asking the same question in different languages. the languages include all claimant languages, as well as english as a control. there are 726 borderlines questions in total for the 251 territories. dataset statistics are given in ## experiments we perform two sets of experiments with border-lines queries to glean insights into the geopolitical bias of llms: first, we compare results between models, for models in which rank classification applies (i.e., all except gpt-4). second, we compare results with modifications to the prompt and focus on a single model, gpt-4 (in which responses are parsed from the short answer response). later, in §7, we present several qualitative case studies. ## qualitative analysis in the prior section, we compared model responses over the entire borderlines dataset. however, the status of each individual disputed territory is complex and has many players and relations at stake. nor is each disputed territory equally inflammatory; compare a lone rock in the middle of a vast ocean versus a densely populated area on the mainland of a continent. we therefore perform qualitative case studies on several notable disputed territories. we first describe the geopolitical situation to give the readers context on the issue. then, we look at how gpt-4's responses change for each of the strategies from §6.2 (full text of these responses are provided in appendix 7). furthermore, we are now able to qualitatively analyze the model's full responses (beyond just the selected claimant), which contain further discussions on the geopolitical situations and provide useful clues for understanding its reasoning. this allows us to see how a model qualifies, or not, its knowledge about the situation, depending on the probing strategy employed in the prompt. ## conclusion the increasing adoption of large language models as not only tools for creative expression but also for summarizing information makes their use highly prone to issues with factual inconsistencies. such tailoring of responses depending on the language can lead to further polarization of society when the topics are of a socio-politically sensitive nature. in this paper, we investigated geopolitical bias in large language models through the lens of territorial disputes. we introduced the borderlines challenge, a first-of-its-kind benchmark dataset that provides a way to evaluate bias in llms that is inherently factual in nature but is heavily influenced by language and politics. we also contribute a suite of evaluation metrics to measure the models' bias on this dataset along different dimensions, checking for both correctness and consistency across languages. we perform an extensive evaluation of various multilingual llms and find that they exhibit substantial geopolitical bias and recall information differently across languages. moreover, our analyses lead to several counter-intuitive findings, such as larger models tending to underperform on the task. we also present approaches to 'nudge' the llms to mitigate or amplify this bias, showcasing their impact through a series of case studies. our paper encourages future llm development to consider tasks such as territorial disputes, with the goal of mitigating negative effects of inconsistent responses for users interacting in different languages. it can inspire several follow-up studies. first, it would be useful to have a multilingual inquiry into the training data, to precisely identify and quantify wherein different territorial claims arise. this can assist in future efforts of curating more cross-lingually consistent training data. we are furthermore hopeful for the prospects of incorporating cross-lingual information retrieval at inference-time to improve their consistency.