4 papers
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…
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…
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…