2 citations · 3 across the 13 of their papers we have counts for
9 papers · 1 filter
Calibrated Typed Decisions for Entity, Relation, and Event Extraction
Yang Wang, Hanlin Xiao, Yanan Ma +2
Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remains challenging. Supporting user…
Reading Between the Frames of Multimodal Drivelology
Yang Wang, Yanan Ma, Yiqi Liu +8
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…
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…
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…
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…
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…
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…
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
Yang Wang, Chenghua Lin
Recent advancements in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. While various defence mechanisms have been pro…
From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls
Tomas Goldsack, Yang Wang, Chenghua Lin +1
This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in re…