Exploring Inherent Biases in LLM s within K orean Social Context: A Comparative Analysis of C hat GPT and GPT -4 (2024)

Abstract: Large Language Models (LLMs) have significantly impacted various fields requiring advanced linguistic understanding, yet concerns regarding their inherent biases and ethical considerations have also increased. Notably, LLMs have been critiqued for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. However, most research analyzing these biases has predominantly focused on communities where English is the primary language, neglecting to consider the cultural and linguistic nuances of other societies. In this paper, we aim to explore the inherent biases and toxicity of LLMs, specifically within the social context of Korea. We devise a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. Our findings indicate that certain personas or prompt combinations consistently yield harmful content, highlighting the potential risks associated with specific persona-issue alignments within the Korean cultural framework. Furthermore, we discover that GPT-4 can produce more than twice the level of toxic content than ChatGPT under certain conditions.

## introduction large language models (llms) acquire comprehensive knowledge to effectively address user intention through instruction and alignment tuning, leveraging extensive text datasets and parameters @xcite @xcite @xcite . in light of these, this approach unavoidably exposes them to biased and potentially harmful content present in the training data. given that llms are designed to generate responses that align with † corresponding author the patterns observed in their training data, the absence of rigorous ethical evaluations poses a notable risk of perpetuating content that could be detrimental, particularly to individuals belonging to socially marginalized groups @xcite @xcite . in response to the inherent risks, the natural language processing (nlp) research community has predominantly directed its efforts toward assessing the ethical implications and fairness of llms, emphasizing thorough scrutiny of these models through various investigations @xcite @xcite . however, the majority of these investigations into llms are centered on english-centric contexts, a constraint arising from the necessity to deeply comprehend the cultural and social intricacies of less-explored languages. this observation implies a potential oversight in catering to the requirements of non-major languages, thereby exposing a gap in ensuring the ethical utilization of llms across diverse linguistic landscapes. such a gap accentuates the susceptibility encountered when generating content in other languages, which in turn may compromise the advancement of more inclusive and considerate language models @xcite @xcite . in this study, our focus is on korean, a language outside the mainstream cultural sphere, to scrutinize the inherent biases and potential harmful effects of llms in the context of societal issues and persona interactions. to this end, we utilize chatgpt @xcite and @xcite for comparison. to systematically analyze and understand the behavior of these models, we construct a comprehensive set of prompts covering six distinct topics (including politics, professions, religion, etc.), each accompanied by detailed issues and personas. by incorporating personas and issues into the prompts, we investigate how the introduction of different personas influences response generation on issues and assess the toxicity levels of the generated content. our analysis reveals a varying sensitivity to the generation of harmful content among the models, depending on the personas and issues involved, with certain combinations consistently resulting in detrimental outcomes. particularly noteworthy is our finding that gpt-4, despite being perceived as a safer option, can produce content with significantly higher levels of toxicity for certain issues compared to chatgpt. this highlights the nuanced dynamics of bias and potential harm inherent within llms and underscores the importance of thorough evaluation and mitigation strategies in their deployment. ## social context-aware persona injection to elicit the inherent toxicity within llms, we engage them in discussions on key societal issues prevalent in korean society, thereby incorporating social context into our analysis. by crafting prompts that probe the models on internal societal issues, we reveal how the nuanced dynamics within society might influence the generation of toxic content by llms. ## findings and analysis chatgpt and gpt-4 exhibit notable differences in their ability to filter toxicity depending on the topic. as shown in gpt-4 is generally safer than chatgpt comparing the scores of chatgpt and gpt-4 as seen in ## conclusion this study examines toxicity and bias in large language models (llms), focusing on their treatment of various social issues within korean society. although chatgpt and gpt-4 are recognized for their relative safety among llms, our analysis reveals considerable fluctuations in toxicity based on the personas and queries applied, highlighting their potential to generate undesirable toxic responses in the korean context. this variability signals a susceptibility to biases on certain topics pertinent to korean society. we notably identify that certain combinations of topics and personas pose a fatal risk of inflicting harm to users, including socially marginalized groups. our findings confirm that even gpt-4 can manifest higher levels of toxicity than chatgpt in specific subject areas. given our findings, a re-evaluation of llm ethics and fairness is crucial. researchers must approach llms cautiously, especially in non-major languages like korean. our study highlights the urgent need to address and reduce model biases.