John vs. Ahmed: Debate-Induced Bias in Multilingual LLMs (2024)

Abstract: Large language models (LLMs) play a crucial role in a wide range of real world applications. However, concerns about their safety and ethical implications are growing. While research on LLM safety is expanding, there is a noticeable gap in evaluating safety across multiple languages, especially in Arabic and Russian. We address this gap by exploring biases in LLMs across different languages and contexts, focusing on GPT-3.5 and Gemini. Through carefully designed argument-based prompts and scenarios in Arabic, English, and Russian, we examine biases in cultural, political, racial, religious, and gender domains. Our findings reveal biases in these domains. In particular, our investigation uncovers subtle biases where each model tends to present winners as those speaking the primary language the model is prompted with. Our study contributes to ongoing efforts to ensure justice and equality in LLM development and emphasizes the importance of further research towards responsible progress in this field.

heading: 1 Introduction
text: Language models have become an essential tool for various applications, from NLP to human-computer interaction. As these models continue to advance, the focus on their safety and ethical implications has become increasingly important. This is especially true for models that cater to specific languages or regions, where cultural nuances and contexts play a significant role in ensuring safe and responsible use (Zhao and Schütze, 2021).
Large language models (LLMs), such as GPT, are initially pretrained on vast amounts of websourced text. This text can contain biases and toxic content, leading to LLMs that may perpetuate these issues when fine-tuned for specific tasks (Dhamala et al., 2021).
Ayoub et al. (2024) investigates biases in large language models (LLMs) in healthcare contexts.
Using simulations with OpenAI’s GPT-4, physicians make life-and-death decisions in resourcescarce scenarios. Results show consistent biases based on demographic traits like race, gender, age, political affiliation, and sexual orientation. Physicians tend to favor patients similar to themselves. For instance, they often save patients with their own demographic characteristics and political affiliation. The study highlights the significant biases in publicly available chatbot LLMs, raising concerns about their potential negative impact on patient outcomes if used in clinical care without precautions.
To mitigate bias and toxicity, methods like Direct Preference Optimization (DPO) (Rafailov et al., 2023) and Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022) have been proposed. These methods align generative models with user intent after the fine-tuning phase, aiming to make the models more helpful, truthful, and harmless. While DPO and RLHF have been deemed effective and are widely-used, there is evidence suggesting that they may only obscure deeply-rooted biases, making such biases less apparent but not necessarily eliminated (Gupta et al., 2024).
Alakrot et al. (2021) analyze the safety of Arabic and English language models and find that Arabic models had higher rates of generating harmful and biased content, particularly in the domains of religion and politics. Wang et al. (2023) empirically study the multilingual safety of four widely used LLMs. The results show that all LLMs produce significantly more unsafe responses for non-English queries than English ones, indicating the necessity of developing safety alignment for non-English languages. These studies underscore the necessity for additional research on the safety of language models, particularly in handling sensitive topics based on prompt language. Furthermore, they emphasize the importance of developing safeguards and implementing best practices to ensure the responsible
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use of these models. Most existing research on bias in language mod-
els is mainly focused on English, leading to a significant research gap concerning other widely spoken languages, such as Arabic and Russian. Consequently, there is a substantial lack of comprehensive studies that explore and understand biases in these languages. Additionally, there is an unexplored area in the methodology of bias research, specifically the use of debate-induced methods to uncover biases in language models. These methods, which involve using argumentative text to provoke and reveal biases, may offer novel insights into how biases are embedded and can be mitigated. This research aims to address these two primary limitations by extending the focus beyond English and incorporating debate-induced methods to provide a deeper understanding of biases in language models.
In this study, we aim to explore language model safety. We focus on evaluating biases across various languages and contexts in GPT-3.51 and Gemini (Google Team et al., 2023). In particular, we propose and utilize a novel debate-based prompting setup that we claim is effective in exposing the deeply-rooted bias in LLMs. We employ a multilingual approach involving Arabic, English, and Russian. Through our designed prompts and scenarios, we attempt to evaluate cultural, political, religious, racial and gender biases in such large models and contribute to the ongoing discourse on fairness and equity in NLP.
The rest of the paper is organized as follows: Section 2 is about related work. We introduce our prompt template that is used to generate the debates in Section 3. In Section 4, we present our methods involving the models we use and how we evaluate them. Our experiments are in Section 5 and Section 6 is a walk-through and discussion of our findings. We conclude in Section 7, list our limitations in Section 8, and discuss ethical considerations in Section 9. We now review related work.

heading: 2 Related Work
text: There have been several recent efforts to examine the safety of language models. Kotek et al. (2023) investigates the presence and impact of gender biases in Large Language Models (LLMs). They present a novel testing paradigm that differs from
1https://platform.openai.com/docs/models/ gpt-3-5-turbo
previous methods, using sentences designed to reveal biases in models’ occupation-related assumptions based on gendered pronouns. Their findings indicate that LLMs are significantly more likely to align occupations with stereotypical gender roles, reflecting societal biases. Moreover, the paper discusses how LLMs often fail to recognize the inherent ambiguity in pronoun references in sentences, leading to biased and factually inaccurate explanations for their choices. Furthermore, Dong et al. (2023) proved explicit and implicit gender bias using a conditional text generation mechanism, concluding that a larger model doesn’t necessarily equate to greater fairness. Stańczak et al. (2023) investigates in large pre-trained language models gender bias towards politicians, which varies significantly among the examined languages.
Arora et al. (2023) utilized Hofstede’s cultural dimensions theory and questions from the World Values Survey to investigate how pre-trained Language Models (PLMs) reflect cultural differences in values. Their study involved converting survey questions into cloze-style probes and running them through three multilingual language models (mBERT, XLM, and XLM-R). They found that the values embedded in these popular multilingual models varied across cultures but only weakly aligned with established values surveys. This discrepancy underscores the complexities and challenges in ensuring PLMs accurately reflect diverse cultural values. Ramezani and Xu (2023) delve into whether English language models possess knowledge concerning moral norms in various countries. Its findings anticipate that empirical moral norms across countries are less favourably compared to previously reported English moral norms. Moreover, in "Having Beer after Prayer? Measuring Cultural Bias in Large Language Models" (Naous et al., 2023), the authors address the critical issue of cultural bias in multilingual and Arabic monolingual Large Language Models (LLMs). They unveil the models’ inclination towards Western culture, demonstrating this bias through a series of experiments involving story generation, Named Entity Recognition (NER), sentiment analysis, and text infilling tasks. Utilizing CAMeL (Obeid et al., 2020), a novel resource consisting of 628 prompts and 20,368 entities spanning Arab and Western cultures, the study reveals LLMs’ tendency to stereotype and misrepresent Arab culture, often favoring Western-centric content. Despite focusing on Arabic contexts, the findings highlight a broader chal-
lenge in ensuring LLMs accurately reflect diverse cultural nuances. Similarly, in Dwivedi et al. (2023) the authors introduce EtiCor, an Etiquettes Corpus comprising texts about social norms from five different global regions. This corpus aims to evaluate Large Language Models’ (LLMs) knowledge and understanding of region-specific etiquettes, highlighting the cultural sensitivity necessary for dayto-day human interactions. Initial experiments with state-of-the-art LLMs reveal their limitations in accurately representing non-Western etiquette, underscoring the models’ inherent biases towards Western norms.
Motoki et al. (2024) employ a novel empirical design to assess political bias within ChatGPT. They analyze ChatGPT’s responses to a set of questions mimicking the Political Compass questionnaire, intending to impersonate political inclinations toward specific parties in the United States, Brazil, and the United Kingdom. The study reveals a significant systematic bias of ChatGPT towards left-leaning political parties: the Democrats in the US, Labor in the UK, and Lula’s supporters in Brazil. This tendency raises concerns about the potential impact of such biases on political and electoral processes. Moreover, Liu et al. (2022) introduced two metrics to measure political bias in GPT-2-generated text: Indirect Bias and Direct Bias. Indirect Bias assesses the political bias of texts generated using neutral prompts by comparing the bias in texts generated with specific keywords to those generated with all keywords in an attribute. Direct Bias evaluates the difference in bias when texts are generated using prompts with explicit political triggers (e.g., "democrat" or "republican"). Their findings revealed a predominant liberal-leaning bias in GPT-2 outputs, influenced by the context’s sensitive attributes. Then it introduces a novel reinforcement learning framework aimed at mitigating such biases without the need to retrain the model or access the original training data. This framework, adaptable to both word-embedding and classifier-guided approaches, effectively reduces political bias across various attributes while maintaining the readability and semantic coherence of the generated text.
Wang et al. (2024) undertake a rigorous examination of trustworthiness in GPT-3.5 and GPT-4 models on multiple dimensions, including toxicity, stereotype bias, adversarial robustness, out-ofdistribution robustness, privacy, machine ethics, and fairness. This work presents a novel framework for evaluating the complex trustworthiness of large
language models (LLMs), revealing significant vulnerabilities, such as the ease of eliciting toxic and biased output, privacy breaches, and susceptibility to adversarial prompts. The study notably finds that while GPT-4 often outperforms GPT-3.5 in standard benchmarks, it is more prone to manipulation through carefully crafted prompts, underscoring the nuanced trade-offs between model capabilities and vulnerabilities. The comprehensive nature of this assessment provides a foundation for efforts to improve the safety, fairness, and reliability of LLMs.
Dhamala et al. (2021) introduce a comprehensive framework to evaluate social biases in openended language generation. The paper presents the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset consisting of 23,679 English text generation prompts across five domains: profession, gender, race, religion, and political ideology. Alongside this dataset, the study proposes new automated metrics for assessing social biases through dimensions of toxicity, psycholinguistic norms, and text gender polarity. Evaluations conducted on texts generated by three popular language models reveal pervasive social biases, with generated content often reflecting stereotypical and prejudiced views, particularly against historically disadvantaged groups.
In a recent paper, Alghamdi et al. (2024) address the critical need for assessing the trustworthiness of Large Language Models (LLMs) in Arabic. Introducing AraTrust, a benchmark for evaluating the trustworthiness of Arabic LLMs, the study spans multiple dimensions including ethics, safety, privacy, and offensive language. AraTrust consists of 516 human-crafted multiple-choice questions covering a wide range of topics relevant to Arabic cultural and societal norms. Evaluations of several LLMs, including GPT-4, AceGPT 7B and Jais 13B, reveal significant disparities in performance, particularly highlighting the superior trustworthiness of proprietary models over open-source alternatives. This work sheds light on the under-explored area of LLM safety and trustworthiness in non-Western languages, marking a step towards the development of safer and more culturally aligned AI systems for Arabic-speaking users.

heading: 3 Data
text: We introduce a novel prompt template that we use to generate model outputs over multiple trials. We
then use the generated output to quantify and evaluate model bias. As seen in Figure 5, our prompt template revolves around generating a debate between two sides representing two opposing viewpoints. This debate-based setup is an indirect way to uncover the deeply rooted bias in the model, bypassing the safety alignment in a similar fashion to Gupta et al. (2024). Constraining the debate outcome to be binary makes quantifying the bias straight-forward. The prompt template is filled in using some of the domain and debate outcome examples that are presented below in Section 4.1.
Across all possible <Domain> combinations, this prompt is provided in three deliberately selected distinct languages for this task: English, Arabic, and Russian. Each language offers unique linguistic nuances and cultural perspectives, enriching the diversity of responses generated. By presenting the prompt in multiple languages, we aim to explore how cultural backgrounds and linguistic contexts influence the portrayal and interpretation of the debate. This multilingual approach ensures a comprehensive examination of the topic across different domains and debate outcomes.

heading: 4 Methods

heading: 4.1 Domains
text: Our primary objective of including multiple domains and sub-domains (shown in Table 1) is to systematically examine and uncover biases in language model responses.
By encompassing diverse cultural, political, racial, religious, and gender-specific contexts, we aim to create a comprehensive set of prompts that could effectively probe the language model’s sensitivity to biases across various dimensions. Each
domain and sub-domain represents a distinct set of societal norms, beliefs, and values, which may influence the language model’s responses. The decision to merge North America and Europe in our tests on cultural biases is based on the similarity in certain socio-cultural aspects and linguistic contexts observed in previous literature (Dwivedi et al., 2023). Moreover, we choose controversial subdomains that are most likely well-represented in the pretraining data. We further validate this by explicitly prompting the model about each sub-domain, and ensure that the output captured the nuances of the subdomain. We set the outcome of the debate to "One side must win". This approach enables us to identify and address potential biases in the language model’s output, thereby contributing to the ongoing efforts to ensure fairness and equity in NLP systems.

heading: 4.2 Models
text: GPT-3.52, OpenAI’s Generative Pre-trained Transformer model, has garnered immense popularity and adoption due to its unprecedented capabilities in NLP. Widely used by developers, researchers, and businesses alike, its availability as an API has democratized access to state-of-the-art language models. This widespread usage underscores the importance of studying and mitigating biases inherent in this model. As an AI system trained on vast amounts of internet text, GPT-3.5 reflects the biases present in the data it learns from, raising concerns about the potential perpetuation or amplification of societal biases. Therefore, understanding and addressing bias in GPT-3.5 is crucial to ensure fair and equitable outcomes across various applications. Additionally, the fact that GPT-3.5’s API is freely accessible to developers further highlights its significance as a transformative tool in the realm of NLP, enabling innovation and exploration in diverse domains.
Gemini (Google Team et al., 2023) models represent a notable evolution in NLP, leveraging Transformer decoder architecture with refined structural enhancements and model optimizations. Gemini excels in NLP and demonstrates remarkable multilingual capabilities. The meticulous design of this family of models underscores a claimed commitment to responsible and ethical deployment, achieved through the implementation of robust safety policies. An intriguing aspect to explore
2https://platform.openai.com/docs/models/ gpt-3-5-turbo
is whether our debate prompting method will unveil any latent biases within the Gemini 1.0 Pro model, shedding light on its adherence to ethical standards and unbiased decision-making.

heading: 4.3 Evaluation
text: First, we prompt the model to generate 20 debates, each with an outcome indicating the winner. Afterwards, we conduct a human review of selected samples to verify the model’s choice of the winner. Our goal is to evaluate the bias of the target LLM and examine its responses in context-specific ways. This involves analyzing how it handles sensitive or controversial topics and its ability to avoid producing harmful or biased content. Human judges evaluate the model’s bias by using a win-rate metric across the 20 debates to trace the pattern. We have chosen this number of debates to eliminate the error of random coincidence and increase confidence (Lin et al., 2024). Thus, our overall score range is from 50 to 100%. A score close to 100% would indicate a consistent preference for one side winning every debate, suggesting biased responses. Conversely, a score near 50% would suggest balanced responses with no discernible bias, as it would reflect an equal chance of winning for both sides.

heading: 5 Experiments
text: We prompted two models with 36 carefully selected questions across five domains in three different languages. Our investigation encompasses a singleturn probing methodology. In single-turn probing, both the cultural context and the probe are provided in a single prompt, facilitating direct assessment. Each prompt is generated 20 times and the win rate is calculated.

heading: 6 Results and Discussion

heading: 6.1 Culture
text: We prompt GPT-3.5 and Gemini with initiating a debate between representatives of two distinct cultures discussing their respective religions. Each prompt is provided in Arabic, Russian and English, leveraging the capabilities of each model, and the outcomes have been compiled.
Appendix A contains detailed tables for each debate, while Figure 2 offers a comprehensive overview of the results. The win rate calculation involves tallying the total wins for each culture in a specific language from Table 3 and 4, then dividing it by the total number of debates conducted against that culture.
Interestingly, both Gemini and GPT-3.5 demonstrate a preference for Latin American culture across all prompted languages, as evident in Figure 2. Conversely, prompting in Russian consistently resulted in outputs opposing European culture, with a zero win rate observed for both GPT3.5 and Gemini.
From the results, it is clear that there are certain biases in the outcomes of the debates. Some cultures consistently perform better when paired with specific counterparts. Specifically, we observe a consistent trend wherein North America-Europe (NE) emerge as the dominant culture, winning debates against all counterparts in the majority of cases. Across multiple cultural pairings, NE consistently achieves higher winning percentages compared to other regions, indicating a pattern of dominance in the model’s predictions when prompted in English. Whether this bias arises from the model, the data it was trained on, or other factors is open to further investigation. However, it is essential to critically assess such biases and consider them when interpreting the results provided by the LLM.
The most striking observation gleaned from the
preceding outcomes, particularly evident when prompts were issued in Arabic using GPT-3.5, is the pronounced escalation in bias favoring one side over the other. Conversely, when prompts were conducted in English, the victory rates display a wider spectrum, ranging from 55% to 100%. Notably, when prompted in Arabic, the success rates consistently reach either 95% or 100% as shown in Table 4.
Our experiments employing the Gemini model also result in intriguing findings. While English prompts yield outcomes consistent with those generated by GPT-3.5, Gemini exhibits reduced susceptibility to bias when prompted in Russian and notably diminished bias when prompted in Arabic compared to GPT-3.5. This observation is evident upon comparing the win percentages in Table 3 to those in Table 4.

heading: 6.2 Religion
text: We investigate religious biases through debates among different religious pairs. The results, summarized in Table 6, highlight the prevailing tendencies within these debates. As depicted in Figure 3, Christianity emerges as the dominant religion across all languages in both models. Conversely, while Hinduism is present in the prompts, it shows a lack of favorable outcomes, not securing wins. Similarly, Judaism, while included in the prompts, does not achieve victory in Arabic language prompts.
As seen in Table 6, some religions significantly outperform others. Specifically, our results show a notable trend favoring Christianity in simulated debates against Islam, Hinduism, and Judaism across English, Russian, and Arabic, with significant win rates. This outcome aligns with an assumed representation prevalence of Christianity on the web, reinforcing the validity of our approach.
Further reinforcing the validity of the method, Islam exhibits a higher success rate in debates con-
ducted in Arabic. This showcases the model’s capacity to mirror the linguistic and cultural contexts likely present in its training data. In contrast, Hinduism appears underrepresented, securing lower win rates when juxtaposed against other religions. This is particularly notable in the Russian-language debates.
These patterns persist upon reiteration of the experiments employing the Gemini model. The win-rate consistency between GPT-3.5 and Gemini stands at 80%, with Christianity markedly prevailing, as shown in Table 7.

heading: 6.3 Politics
text: Table 5 presents the outcomes of political bias experiments, which simulated debates between representatives of opposing political sides. Notably, in English-language simulations, an Israeli perspective prevails over a Palestinian with a high win rate of 80%. Meanwhile, debates involving Indian vs. Pakistani and Russian vs. Ukrainian perspectives resulted in absolute wins for the former party in each case, respectively, with win rates of 100%. This suggests a potential alignment bias in the model towards these particular national perspectives within the English language context.
The results in Arabic and Russian tell a different story. In Arabic, the Palestinian perspective turned the tables with a 60% win rate against the Israeli, while in Russian, the Ukrainian viewpoint emerge with a notably high win rate of 95% against the Russian. The variance across languages underscores the model’s differentiated linguistic and cultural processing capabilities, which may reflect the nuanced geopolitical narratives prevalent in the training data for each language. These disparate outcomes highlight the necessity of examining the contextual influence of language data used for training on AI models, as it may significantly sway the representation of political perspectives in modelgenerated content.

heading: 6.4 Gender
text: Here, we select two different prompt topics to discuss as presented in Figure 5. For each of these topics, the models generate dialogues where a woman and man express their ideas. However, in most cases, a woman speaks out for the suppressed rights of women. Table 2 presents the outcomes of simulated debates on these topics.
These debates are generated in Arabic, English, and Russian and show almost similar results. In
other words, for debates on gender identity, the results indicate a clear bias toward women in all three languages. Women win the debate 100% in all instances. Similarly, when a man and a woman are discussing opportunities to become a doctor, women maintain a significant advantage. However, in English debates, women win 80% of the time, while in Russian and Arabic debates, they win in 100% of the debates.
These findings point to a potential bias within the GPT-3.5 model, demonstrating a tendency to favor women in discussions pertaining gender identity and career prospects. While biases are less pronounced when prompting with Gemini, particularly noticeable in Arabic debates as presented in Table 2, it remains evident that both GPT-3.5 and Gemini exhibit a general inclination toward favoring women in their responses.

heading: 6.5 Race
text: The analysis of win rates across race pairs, as depicted in both the Arabic and English in Table 8 generated by GPT-3.5 with a prompt about immigration policies, reveals notable biases in the representation of racial groups within the context of immigration. Both tables consistently show high win rates for Asians across various race pairs. In both models, without any particular ranking, the top three races are Middle Eastern, Asian, and African American. There is minimal favoritism towards the White race when using Gemini for prompts, and similarly, limited favor towards the Black race
in GPT-3.5 when English is the language of the prompt.
This could indicate a potential bias towards Asians, suggesting that they are more likely to be favored or perceived as winners in the context of immigration policies. In English, Whites have relatively high win rates when paired with other races such as Asians and Blacks. However, in Arabic, White representation is lower, especially when compared to the win rates of other races like Asians and Middle Easterners.
When prompting in English, Middle Easterners (referred to as ME) show moderate win rates. However, when prompting in Arabic, they have notably high win rates when paired with African Americans and Whites. This could suggest a bias towards Middle Easterners in certain contexts within immigration policies. Europeans (referred to as EU) generally have moderate to high win rates in both tables, indicating a relatively favorable perception compared to some other races.
Arabic and English languages generally show similar trends in terms of which races have higher win rates across different race pairs. This suggests some consistency in the biases portrayed across the two languages as shown in Figure 6. The extent of bias can vary across languages, as seen in the comparison between Arabic and English. For instance, prompting in Arabic appears to yield higher success rates for Middle Easterners compared to English prompts. Conversely, when prompted in Russian, GPT-3.5 exhibits a consistent trend, whereas Gemini showed a distinct pattern, showing no preference among Asian, White, and Black individuals, yet displaying lower success rates for Middle Easterners.
We carry out further analysis by prompting Gemini on two distinct topics: immigration and child labor, as illustrated in Table 9 and Table 10 respectively. In English prompts on immigration, Gemini exhibits a preference for Black individuals more frequently, prevailing in three out of five instances.
Meanwhile, GPT-3.5 shows a bias against Black individuals, with zero wins out of five debates. However, in Arabic prompts, both Gemini and GPT-3.5 demonstrate notable biases, albeit towards different cultures. Notably, while GPT-3.5 favors Asian cultures, Gemini displays a distinct bias pattern.
On the topic of child labor, English prompts result in relatively moderate bias, with win rates hovering around 55%. Conversely, a stark contrast is observed in Arabic prompts, where win rates exceeded 95% consistent with that observed across other domains.

heading: 7 Conclusion
text: Drawing on our crafted prompts for story generation, we leveraged debates as a probing mechanism to unveil biases inherent in GPT-3.5 and Gemini. Across cultural, political, racial, religious, and gender domains, we meticulously scrutinized the coherence of responses from these conversational agents. Our comprehensive analysis uncovered a discernible inclination within both ChatGPT and Gemini, particularly evident in their determination of debate outcomes related to specific cultures, religions, or political affiliations. Interestingly, these biases were not readily apparent without the structured framework of debate. Notably, a significant bias was observed when prompting in Arabic, regardless of the topic or domain. Conversely, Russian prompts yielded the most neutral outcomes when compared to English and Arabic in both GPT3.5 and Gemini. These findings underscore the persistent biases in models despite efforts to align and eliminate them, highlighting the imperative to enhance model safety precautions. As a direction for future work, further investigation into refining and augmenting these safety measures is warranted.

heading: 8 Limitations
text: While this study contributes significant insights into biases present in LLMs across different lan-
guages and contexts, there are inherent limitations that should be acknowledged. First, the examination of biases is primarily focused on cultural, political, racial, religious, and gender domains, which may not encompass the entirety of potential biases that exist within LLMs. Second, while there are currently several main stream models, the study is limited to GPT-3.5 and Gemini. Third, while carefully designed prompts and scenarios are utilized in Arabic, English, and Russian, this is only a short list of languages. Finally, the uncovering of subtle biases, particularly in favor of speakers of the primary language the model is prompted with, suggests the need for further investigation into the mechanisms underlying these biases and their potential impact on real-world applications. Our work does not investigate such mechanisms. Despite these limitations, our work contributes to ongoing efforts to promote justice and equality in NLP and emphasizes the importance of continued research into the responsible development of LLMs.

heading: 9 Ethics Statement
text: Intended Use. Our study aims to shed light on the biases present in language models across different languages and contexts, particularly focusing on GPT-3.5 and Gemini. The findings of this research underscore the importance of evaluating safety and ethical implications in NLP, especially concerning cultural, political, racial, religious, and gender domains. We anticipate that our work will stimulate further research into ensuring the responsible development and deployment of LLMs in diverse linguistic and socio-cultural contexts.

heading: Acknowledgments
text: We acknowledge support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), Digital Research Alliance of Canada,3 and UBC Advanced Research Computing-Sockeye.4
3https://alliancecan.ca 4https://arc.ubc.ca/compute-storage/
ubc-arc-sockeye

heading: A Appendix
text: Culture Opponents Winner Win Rate English

heading: EA - IN IN 70%

heading: EA - MEA MEA 90%

heading: EA - NE NE 55%

heading: EA - LA LA 80%

heading: IN - MEA MEA 65%

heading: IN - NE NE 60%

heading: IN - LA LA 75% MEA - NE NE 85% MEA - LA LA 65%

heading: NE - LA LA 55%
text: Russian

heading: EA - NE EA 75%

heading: EA - MEA EA 70%

heading: EA - IN IN 55%

heading: A KXA J̄ @ ú

heading: Future Work
text: As a direction for future work, further investigation into refining and augmenting these safety measures is warranted