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20242026
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cs.CL2026

Reading Between the Frames: Interpreting Implicit and Non-literal Meaning in Social Media Videos

Yang Wang, Yanan Ma, Yiqi Liu +7

Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the int…

cs.CL2026

Hybrid Adversarial Defence for Natural Language Understanding Tasks

Manar Abouzaid, Yang Wang, Chenghua Lin +1

Large Language Models (LLMs) are vulnerable both to hallucination and adversarial manipulation. Although these problems are closely related, existing defences typically address the…

cs.CL2026

Evaluating LLM-Based Grant Proposal Review via Structured Perturbations

William Thorne, Joseph James, Yang Wang +2

As AI-assisted grant proposals outpace manual review capacity in a kind of ``Malthusian trap'' for the research ecosystem, this paper investigates the capabilities and limitations…

cs.CL2025

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth

Yang Wang, Chenghao Xiao, Chia-Yi Hsiao +4

We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth" - utterances that are syntactically coherent yet pragmatically paradoxical, emotiona…

cs.CL2025

Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal

Yang Wang, Chenghao Xiao, Yizhi Li +3

Pre-trained language models (PLMs) have driven substantial progress in natural language processing but remain vulnerable to adversarial attacks, raising concerns about their robust…

cs.CL2025

Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts

Hanhua Hong, Chenghao Xiao, Yang Wang +3

Evaluating natural language generation systems is challenging due to the diversity of valid outputs. While human evaluation is the gold standard, it suffers from inconsistencies, l…