arXiv subjects

Mohammadamin Shafiei

Publications and source records attributed to Mohammadamin Shafiei.

6 recordsLinked to original sources

arXiv2026

Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues

As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficial. We show that current fairness evaluations substantially overestimate moral safety. Models appear fair when demographic identity is stated as an explicit label, yet become measurably less fair when the same identity must be inferred. We term this failure performative compliance, where a model is fair when the presentation resembles a fairness evaluation and less fair as that cue weakens. We introduce a cue-variation methodology that holds the moral dilemma and the demographic identity fixed and varies only how that identity is conveyed. Hiding the explicit label raises harmful decisions by +4.4 pp, changes model safety rankings, and the shift persists when models correctly infer the demographic, ruling out attribution error. We propose the Cue Visibility Gap, a model-agnostic robustness metric that can be added to any existing fairness benchmark to separate genuine from performative moral safety. Fairness evaluations that omit cue variation measure surface compliance, not moral robustness, and should not ground deployment decisions in high-stakes settings.

cs.CL↗

arXiv2025

Beyond Hate Speech: NLP's Challenges and Opportunities in Uncovering Dehumanizing Language

Dehumanization, i.e., denying human qualities to individuals or groups, is a particularly harmful form of hate speech that can normalize violence against marginalized communities. Despite advances in NLP for detecting general hate speech, approaches to identifying dehumanizing language remain limited due to scarce annotated data and the subtle nature of such expressions. In this work, we systematically evaluate four state-of-the-art large language models (LLMs) - Claude, GPT, Mistral, and Qwen - for dehumanization detection. Our results show that only one model-Claude-achieves strong performance (over 80% F1) under an optimized configuration, while others, despite their capabilities, perform only moderately. Performance drops further when distinguishing dehumanization from related hate types such as derogation. We also identify systematic disparities across target groups: models tend to over-predict dehumanization for some identities (e.g., Gay men), while under-identifying it for others (e.g., Refugees). These findings motivate the need for systematic, group-level evaluation when applying pretrained language models to dehumanization detection tasks.

cs.CL↗

arXiv2025

Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor

With the recent advances in Artificial Intelligence (AI) and Large Language Models (LLMs), the automation of daily tasks, like automatic writing, is getting more and more attention. Hence, efforts have focused on aligning LLMs with human values, yet humor, particularly professional industrial humor used in workplaces, has been largely neglected. To address this, we develop a dataset of professional humor statements along with features that determine the appropriateness of each statement. Our evaluation of five LLMs shows that LLMs often struggle to judge the appropriateness of humor accurately.

cs.CL↗

arXiv2025

MultiHoax: A Dataset of Multi-hop False-Premise Questions

As Large Language Models are increasingly deployed in high-stakes domains, their ability to detect false assumptions and reason critically is crucial for ensuring reliable outputs. False-premise questions (FPQs) serve as an important evaluation method by exposing cases where flawed assumptions lead to incorrect responses. While existing benchmarks focus on single-hop FPQs, real-world reasoning often requires multi-hop inference, where models must verify consistency across multiple reasoning steps rather than relying on surface-level cues. To address this gap, we introduce MultiHoax, a benchmark for evaluating LLMs' ability to handle false premises in complex, multi-step reasoning tasks. Our dataset spans seven countries and ten diverse knowledge categories, using Wikipedia as the primary knowledge source to enable factual reasoning across regions. Experiments reveal that state-of-the-art LLMs struggle to detect false premises across different countries, knowledge categories, and multi-hop reasoning types, highlighting the need for improved false premise detection and more robust multi-hop reasoning capabilities in LLMs.

cs.CL↗

arXiv2025

More or Less Wrong: A Benchmark for Directional Bias in LLM Comparative Reasoning

Large language models (LLMs) are known to be sensitive to input phrasing, but the mechanisms by which semantic cues shape reasoning remain poorly understood. We investigate this phenomenon in the context of comparative math problems with objective ground truth, revealing a consistent and directional framing bias: logically equivalent questions containing the words ``more'', ``less'', or ``equal'' systematically steer predictions in the direction of the framing term. To study this effect, we introduce MathComp, a controlled benchmark of 300 comparison scenarios, each evaluated under 14 prompt variants across three LLM families. We find that model errors frequently reflect linguistic steering, systematic shifts toward the comparative term present in the prompt. Chain-of-thought prompting reduces these biases, but its effectiveness varies: free-form reasoning is more robust, while structured formats may preserve or reintroduce directional drift. Finally, we show that including demographic identity terms (e.g., ``a woman'', ``a Black person'') in input scenarios amplifies directional drift, despite identical underlying quantities, highlighting the interplay between semantic framing and social referents. These findings expose critical blind spots in standard evaluation and motivate framing-aware benchmarks for diagnosing reasoning robustness and fairness in LLMs.

cs.CL↗

arXiv2025

Can I introduce my boyfriend to my grandmother? Evaluating Large Language Models Capabilities on Iranian Social Norm Classification

Creating globally inclusive AI systems demands datasets reflecting diverse social norms. Iran, with its unique cultural blend, offers an ideal case study, with Farsi adding linguistic complexity. In this work, we introduce the Iranian Social Norms (ISN) dataset, a novel collection of 1,699 Iranian social norms, including environments, demographic features, and scope annotation, alongside English translations. Our evaluation of 6 Large Language Models (LLMs) in classifying Iranian social norms, using a variety of prompts, uncovered critical insights into the impact of geographic and linguistic context. Results revealed a substantial performance gap in LLMs' comprehension of Iranian norms. Notably, while the geographic context in English prompts enhanced the performance, this effect was absent in Farsi, pointing to nuanced linguistic challenges. Particularly, performance was significantly worse for Iran-specific norms, emphasizing the importance of culturally tailored datasets. As the first Farsi dataset for social norm classification, ISN will facilitate crucial cross-cultural analyses, shedding light on how values differ across contexts and cultures.

cs.SI↗