--Detecting hate speech in non-direct forms, such as irony, sarcasm, and innuendos, remains a persistent challenge for social networks. Although sarcasm and hate speech are regarded as distinct expressions, our work explores whether integrating sarcasm as a pre-training step improves implicit hate speech detection and, by extension, explicit hate speech detection. Incorporating samples from ETHOS, Sarcasm on Reddit, and Implicit Hate Corpus, we devised two training strategies to compare the effectiveness of sarcasm pre-training on a CNN+LSTM and BERT+BiLSTM model. The first strategy is a single-step training approach, where a model trained only on sarcasm is then tested on hate speech. The second strategy uses sequential transfer learning to fine-tune models for sarcasm, implicit hate, and explicit hate. Our results show that sarcasm pre-training improved the BERT+BiLSTM's recall by 9.7%, AUC by 7.8%, and F1-score by 6% on ETHOS. On the Implicit Hate Corpus, precision increased by 7.8% when tested only on implicit samples. By incorporating sarcasm into the training process, we show that models can more effectively detect both implicit and explicit hate. Note: This paper contains offensive and derogatory language shown only for demonstration. A key challenge in specialized machine learning is the lack of sufficient data for a given task.

In the digital era, individuals are increasingly exposed to online harms such as toxicity, manipulation, and grooming, which often pose emotional and safety risks. Existing systems for detecting abusive content or issuing safety alerts operate in isolation and rarely combine digital safety with emotional well-being. In this paper, we present SafeSpace, a unified web application that integrates three modules: (1) toxicity detection in chats and screenshots using NLP models and Google's Perspective API, (2) a configurable safety ping system that issues emergency alerts with the user's live location (longitude and latitude) via SMTP-based emails when check-ins are missed or SOS alerts are manually triggered, and (3) a reflective questionnaire that evaluates relationship health and emotional resilience. The system employs Firebase for alert management and a modular architecture designed for usability, privacy, and scalability. The experimental evaluation shows 93% precision in toxicity detection, 100% reliability in safety alerts under emulator tests, and 92% alignment between automated and manual questionnaire scoring. SafeSpace, implemented as a web application, demonstrates the feasibility of integrating detection, protection, and reflection within a single platform, with future deployment envisioned as a mobile application for broader accessibility.

Entities in discourse vary broadly in salience: main participants, objects and locations are noticeable and memorable, while tangential ones are less important and quickly forgotten, raising questions about how humans signal and infer relative salience. Using a graded operationalization of salience based on summary-worthiness in multiple summaries of a discourse, this paper explores data from 24 spoken and written genres of English to extract a multifactorial complex of overt and implicit linguistic cues, such as recurring subjecthood or definiteness, discourse relations and hierarchy across utterances, as well as pragmatic functional inferences based on genre and communicative intent. Tackling the question 'how is the degree of salience expressed for each and every entity mentioned?' our results show that while previous approaches to salience all correlate with our salience scores to some extent, no single generalization is without exceptions, and the phenomenon cuts across all levels of linguistic representation.

Large Language Models can emulate different writing styles, ranging from composing poetry that appears indistinguishable from that of famous poets to using slan g that can convince people that they are chatting with a human online . While differences in style may not always be visible to the untrained eye, we can generally distinguish the writing of different people, like a linguistic fingerprint. This work examines whether a language model can also be linked to a specific fingerprint . Through stylometric and multidimensional register analys e s, w e compare human - authored and model - authored texts from different registers. We find that the model can successfully adapt its style depending on whether it is prompted to produce a Wikipedia entry vs. a college essay, but not in a way that makes it indistinguishable from human s . Concretely, the model shows more limited variation when producing outputs in different registers. O ur results suggest that the model prefers nouns to verbs, thus showing a distinct linguistic backbone from humans, who tend to anchor language in the highly grammaticalized dimensions of tense, aspect, and mood . It is possible that the more complex domains of grammar reflect a mode of thought unique to humans, thus acting as a litmus test for Artificial Intelligence. 2 Introduction Scholars from different disciplines have been addressing the question of what makes us human for centuries. For Nobel laureate Bertrand Russell, the answer is language, for "no matter how eloquently a dog may bark, he cannot tell you that his parents were poor but honest". H uman language is both flexible and constrained at the same time, and this is why the Turing Test, described as a litmus test for Artificial Intelligence [ Shieber 199 4, French 200 0], is linked to achieving a level of conversational proficiency that is highly complex, akin to that of a human [ Turing 1950 ] . Human language is flexible in the sense that we all make different choices when conversing. Every human is thought t o have a distinct linguistic fingerprint called idiolect [ Halliday et al. 196 4, Coulthard 2004 ] . This idiolect, which can be defined as an individual's unique use of linguistic forms (including lexical choices, collocations and fixed expressions, punctuation patterns, misspellings, and grammatical style), is critical for authorship attribution in a range of situations: from identifying that a poem with dashes, elliptical syntax, and unconventional capitalization is more likely authored by Emily Dickinson and not by William Shakespeare, to pinning down a person of interest in the course of a criminal investigation, as happened in the Unabomber case .

Legal judgment prediction (LJP) has become increasingly important in the legal field. In this paper, we identify that existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge. Therefore, we introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules, thereby improving the breadth and depth of reasoning. Experiments conducted on the real-world dataset verify the effectiveness of our method. Furthermore, the reasoning chain generated during the analysis process can increase interpretability and provide the possibility for practical applications.

The rapid advancement of large language models (LLMs) has significantly propelled progress in natural language processing (NLP). However, their effectiveness in specialized, low-resource domains-such as Arabic legal contexts-remains limited. This paper introduces MizanQA (pronounced Mizan, meaning "scale" in Arabic, a universal symbol of justice), a benchmark designed to evaluate LLMs on Moroccan legal question answering (QA) tasks, characterised by rich linguistic and legal complexity. The dataset draws on Modern Standard Arabic, Islamic Maliki jurisprudence, Moroccan customary law, and French legal influences. Comprising over 1,700 multiple-choice questions, including multi-answer formats, MizanQA captures the nuances of authentic legal reasoning. Benchmarking experiments with multilingual and Arabic-focused LLMs reveal substantial performance gaps, highlighting the need for tailored evaluation metrics and culturally grounded, domain-specific LLM development.

We constructed JaParaPat (Japanese-English Parallel Patent Application Corpus), a bilingual corpus of more than 300 million Japanese-English sentence pairs from patent applications published in Japan and the United States from 2000 to 2021. We obtained the publication of unexamined patent applications from the Japan Patent Office (JPO) and the United States Patent and Trademark Office (USPTO). We also obtained patent family information from the DOCDB, that is a bibliographic database maintained by the European Patent Office (EPO). We extracted approximately 1.4M Japanese-English document pairs, which are translations of each other based on the patent families, and extracted about 350M sentence pairs from the document pairs using a translation-based sentence alignment method whose initial translation model is bootstrapped from a dictionary-based sentence alignment method. We experimentally improved the accuracy of the patent translations by 20 bleu points by adding more than 300M sentence pairs obtained from patent applications to 22M sentence pairs obtained from the web.

It studies reasoning patterns and logical properties that are not suitably captured by classical propositional or first-order logic. Various logic formalisms have been proposed to handle deontic and normative reasoning, including systems based on modal logics (von Wright, 1951), dyadic deontic logic (Gabbay et al., 2013), and norm-based systems (Hansen, 2014). These systems differ in the properties of the obligation operator, and in their ability to consistently handle deontic paradoxes and/or norm conflicts (Gabbay et al., 2013). Input/Output (I/O) logics (Makinson & van der Torre, 2000) are a particular norm-based family of systems in which conditional norms are represented by pairs of formulas. The pairs do not carry truth-values themselves. I/O logics use an operational semantics based on detachment and come with a variety of different systems, formalized by different so-called output operators . Given a set of conditional norms N, and a set of formulas describing the situational context A, output operators produce a set of formulas that represent the obligations that are in force for that context. In order to check whether some state of affairs φ is obligatory, it suffices to check whether φ out (N, A), where out is some output operator. Unconstrained I/O logics are monotone and cannot consistently handle norm conflicts (i.e., situations in which norms with conflicting obligations are in force) without

Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific ideologies. We investigate how adopting synthetic personas influences ideological expression in LLMs across seven models (7B-70B+ parameters) from multiple families, using the Political Compass Test as a standardized probe. Our analysis reveals four consistent patterns: (i) larger models display broader and more polarized implicit ideological coverage; (ii) susceptibility to explicit ideological cues grows with scale; (iii) models respond more strongly to right-authoritarian than to left-libertarian priming; and (iv) thematic content in persona descriptions induces systematic and predictable ideological shifts, which amplify with size. These findings indicate that both scale and persona content shape LLM political behavior. As such systems enter decision-making, educational, and policy contexts, their latent ideological malleability demands attention to safeguard fairness, transparency, and safety.

Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.