arXiv:2610.28337v1 Announce Type: cross Abstract: Emoji are widely treated as compact affective tokens whose meaning is summarised by a single sentiment score. We argue that this picture is incomplet...
The paper audits a multilingual affective generation benchmark that uses emoji summaries for Bangla, English, and Hindi sentences. It finds that the benchmark’s conclusions are largely artifacts of the measurement instrument, with no system significantly outperforming another when annotators are treated as random factors. The study shows that annotator identity and output length drive most variance, and proposes a new stable metric called emoji‑affect decodability.
The paper investigates how semantic distance and ambiguity contribute to joke humor by revisiting and extending metrics from prior work. It introduces a new symmetry metric—measuring how close the ambiguous element Z is to both X and Y—and evaluates it using two embedding models on three joke datasets, including expanded versions with paired ambiguous sentences. Although models based on these metrics performed poorly in predicting humor ratings, the symmetry metric consistently correlated with higher-rated jokes, hinting it captures a key, though not sole, property of humor.
The study examines Urdu light verbs, which add schematic event meaning while staying lexically linked to their main verbs. Using contextual embeddings from UrduBERT, DunbaaBERT, and multilingual BERT on 1,126 sentences, the authors find significant representational separation between main and light uses across all verb–model pairs, yet main and light uses of the same lemma remain closer than mismatched pairs. In a seven‑way prediction task limited to light uses, UrduBERT achieves 0.866 accuracy and 0.852 macro‑F1, and maintains 0.782 accuracy when tested on unseen preceding forms, demonstrating generalization beyond local verb combinations.
The paper surveys the state of Explainable AI (XAI) in Arabic NLP, highlighting three gaps: a method gap where Arabic XAI relies mainly on limited post‑hoc techniques; a task gap with most work focused on classification tasks and little on generation, retrieval, or dialogue; and a linguistic gap where explanations rarely address Arabic‑specific phenomena such as morphology, dialects, and diglossia. It proposes a taxonomy of tasks, methods, linguistic units, and evaluation practices, and outlines a research agenda for linguistically grounded Arabic XAI.
arXiv:2603.04317v2 Announce Type: replace-cross Abstract: A growing literature shows that variables can be linearly decoded from the activations of large language models (LLMs). These range from prop...
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
arXiv:2606.27314v2 Announce Type: replace Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive m...
The study demonstrates that text produced by large language models (LLMs) leaves a distinct stylometric footprint—primarily increased entropy and lexical diversity—across multiple models and domains. In contrast, AI editing of human text does not replicate this footprint; edited texts show only modest lexical diversity gains and reduced entropy, with lexical density emerging as the key distinguishing feature. Consequently, stylometric analysis can differentiate AI-generated from AI-edited content, but is less effective at distinguishing either from purely human writing.
arXiv:2606. 07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift.
arXiv:2610.15795v1 Announce Type: cross Abstract: Automated moderation systems are deployed at scale, yet they remain opaque to the users and moderators affected by their decisions, and their coverag...
arXiv:2603. 18482v2 Announce Type: replace-cross Abstract: Standard decoding strategies for text generation, including top-$k$, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting outputs to high-probability regions.