S we E val: Do LLM s Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use (2025)

Abstract: Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual messages. Deploying such models across different regions requires them to understand diverse cultural and linguistic contexts and generate safe and respectful responses. For enterprise applications, it is crucial to mitigate reputational risks, maintain trust, and ensure compliance by effectively identifying and handling unsafe or offensive language. To address this, we introduce SweEval, a benchmark simulating real-world scenarios with variations in tone (positive or negative) and context (formal or informal). The prompts explicitly instruct the model to include specific swear words while completing the task. This benchmark evaluates whether LLMs comply with or resist such inappropriate instructions and assesses their alignment with ethical frameworks, cultural nuances, and language comprehension capabilities. In order to advance research in building ethically aligned AI systems for enterprise use and beyond, we release the dataset and code: https://github.com/amitbcp/multilingual_profanity.

## introduction the ability of large language models (llms) to generate human-like text has led to their adoption in various tasks, including text generation @xcite ), text classification @xcite , writing assistance @xcite , code generation @xcite , question answering @xcite and machine translation @xcite , among others. at the same time, large multimodal models are gaining prominence, extending ai's reach beyond text to data modalities such as images and audio @xcite . they have also been utilized to generate synthetic datasets for tasks like data augmentation and document-based applications @xcite . the growing popularity of llms stems from their versatility and applicability across languages. while english has approximately 350 million native speakers, languages like hindi (615 million), spanish (486 million), and french (250 million) often have larger speaker bases. this has led to a push for multilingual llms, which aim to break language barriers and enhance accessibility for non-english speakers. as these models are deployed in diverse regions, ensuring their safety and ethical behavior across languages and cultures is crucial. the safety evaluation of llms has emerged as a critical focus of recent research. various benchmark datasets have been developed to address this challenge. for instance, @xcite provides multi-level safety alignment data across 19 harm categories, such as harassment and hate speech. toxicchat @xcite focuses on toxic behaviors in user-ai interactions, emphasizing conversational contexts often overlooked by traditional toxicity detectors. @xcite evaluates harm scenarios, including offensive jokes and harassment, providing insights into the contextual vulnerabilities of llms. @xcite categorizes safety risks into hierarchical dimensions to better understand implicit and explicit harms. xstest @xcite highlights multilingual and cross-cultural vulnerabilities, an essential consideration for globally deployed llms. additionally, safetybench @xcite and @xcite address both explicit and implicit harms, focusing on challenges such as hate speech, bias, and toxicity. while previous research primarily focuses on explicit harms such as hate speech and harassment, subtler issues like swearing and profanity, which can have significant cultural and ethical impacts, are often overlooked. swear words, frequently used to express strong emotions, vary in perceived severity across cultures-ranging from mild and acceptable to deeply offensive and harmful. this cultural nuance highlights the critical need to assess llms for their ability to handle such language appropriately. our benchmark aims to bridge this gap by explicitly targeting these underexplored areas, focusing on the contextual appropriateness of llm responses. this approach enables a more comprehensive evaluation of llm safety and contributes to advancing the holistic assessment of ethical ai across diverse linguistic and cultural contexts. in summary, the main contributions of our work: ## the sweeval benchmark the sweeval benchmark contains various realworld scenarios to evaluate llms such as variation in writing tone, and context. we manually created a dataset of instruction prompts relevant to both enterprise and casual contexts, such as drafting emails, answering customer queries, sales pitches, and social messages. each task contains prompts with varied tones (positive and negative). in total, we designed 109 english prompts for formal and informal tasks. as llms are deployed in different regions, we selected 25 swear words from both high-resource and low-resource languages: (english (en), spanish (es), french (fr), german (de), hindi (hi), marathi (mr), bengali (bn), and gujarati (gu)), to ensure the dataset evaluates the models' understanding of local linguistic nuances and cultural sensitivities. for a detailed explanation of tone design, swear word selection, and cultural considerations, refer to appendix a.2. task e-mail, sales pitch, research draft etc. tone positive and negative context formal and informal swear words 25 each for en, es, fr, de, hi, mr, bn, gu english prompts 109 final prompts per language 2725 ## conclusion in this paper, we introduce sweeval, a novel benchmark to evaluate llms ability to handle swearing under different contexts and tones. we focus on the ethical and complicated aspect of swearing, especially in low and mid resource languages, across different writing styles. unlike existing benchmarks, sweeval gives priority to the situational intricacies of swearing, making it a valuable tool for assessing language models' ethical and contextual reasoning capabilities. our findings demonstrate that, particularly in multilingual settings, llms' limited reasoning skills and lack of cultural awareness cause them to rarely comprehend swearing and hence respond with such words. we stress the significance of improved training techniques, careful data selection, and better safeguards-not just in english, but for all languages-in order to close this gap.