Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse.While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLMgenerated texts.To this end, we present an analysis framework comparing human-and LLM-authored arguments using two easilyinterpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies).Applied to /r/ChangeMyView arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity -particularly in anticipation and trust.They more closely resemble textbook-quality arguments -cogent, justified, explicitly respectful toward others, and positive in tone.Moreover, counter-arguments generated by LLMs converge more closely with the original post's style and quality than those written by humans.Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV.Milad Alshomary and Henning Wachsmuth.2023. Conclusion-based counter-argument generation. In

Warning: This paper contains content that may be offensive or upsetting.Offensive speech is highly prevalent on online platforms.Being trained on online data, Large Language Models (LLMs) display undesirable behaviors, such as generating harmful text or failing to recognize it.Despite these shortcomings, the models are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many more.Furthermore, the research explores using LLMs in applications with immense social risk, such as latelife companions and online content moderators.Despite the potential harms from LLMs in such applications, whether LLMs can reliably identify offensive speech and how they behave when they fail are open questions.This work addresses these questions by probing sixteen widely used LLMs and showing that most fail to identify (non-)offensive online language.Our experiments reveal undesirable behavior patterns in the context of offensive speech detection, such as erroneous response generation, over-reliance on profanity, and failure to recognize stereotypes.Our work highlights the need for extensive documentation of model reliability, particularly in terms of the ability to detect offensive language.

Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL).However, due to the weak association between the web-collected image-text pairs, models fail to show a fine-grained understanding of the combined semantics of these modalities.To address this issue we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative captions for ITM training towards achieving fine-grained cross-modal comprehension in VL.Additionally, we provide a challenging manually-created test set for benchmarking models on a fine-grained cross-modal mismatch task with varying levels of compositional complexity.Our results show the effectiveness of training on HNC by improving the models' zero-shot capabilities in detecting mismatches on diagnostic tasks and performing robustly under noisy visual input scenarios.Also, we demonstrate that HNC models yield a comparable or better initialization for fine-tuning.Our code and data are publicly available.