arXiv AI July 14, 2026 By Muhammad Abdullahi Said

Polarization Detection: A Hybrid Approach with AfroXLMR-Social and DeBERTa for Low- and High-Resource Settings

arXiv:2607. 10312v1 Announce Type: cross Abstract: The rapid proliferation of online polarization threatens social cohesion, necessitating robust automated detection systems that operate effectively across diverse linguistic contexts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

fine-tuning

arXiv Machine Learning

Jun 3

Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection

arXiv:2606. 03334v1 Announce Type: cross Abstract: Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation identification.

By Pritam Kadasi, Anuj Tiwari, Mayank Singh

nlp

arXiv AI

Jul 7

Polarity Detection of Sustainable Development Goals in News Text

arXiv:2509. 19833v4 Announce Type: replace-cross Abstract: The United Nations' Sustainable Development Goals (SDGs) provide a globally recognised framework for addressing major societal, environmental, and economic challenges.

By Andrea Cadeddu, Alessandro Chessa, Vincenzo De Leo, Gianni Fenu, Francesco Osborne, Diego Reforgiato Recupero, Angelo Salatino, Luca Secchi

llms nlp fine-tuning benchmarks

arXiv AI

Jul 9

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.

By Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu

llms nlp efficiency multimodal benchmarks

arXiv AI

Jul 17

Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach

arXiv:2607. 14957v1 Announce Type: new Abstract: Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage.

By Besim Shala, Peter Mandl, Andreas Humpe, Martin H\"ausl

llms

arXiv Computation and Language

Sep 22

The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness

arXiv:2605.28190v2 Announce Type: replace Abstract: Embedding benchmarks like MTEB report a single score per model, implicitly treating robustness as a static, scalar property. We argue that embeddin...

By Manuel Frank, Haithem Afli

llms rag benchmarks

arXiv Computation and Language

Sep 22

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...

By Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin, Simon Ostermann

llms fine-tuning

One email a morning, machine-written

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Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection

Polarity Detection of Sustainable Development Goals in News Text

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach

The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation