8 papers
AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications
Honglin Mu, Jinghao Liu, Kaiyang Wan +4
Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content moderation. However, our research identi…
COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations
Rui Xing, Preslav Nakov, Timothy Baldwin +1
Fact-checking on major platforms, such as X, Meta, and TikTok, is shifting from expert-driven verification to a community-based setup, where users contribute explanatory notes to c…
On the Interplay between Human Label Variation and Model Fairness
Kemal Kurniawan, Meladel Mistica, Timothy Baldwin +1
The impact of human label variation (HLV) on model fairness is an unexplored topic. This paper examines the interplay by comparing training on majority-vote labels with a range of…
An Analytical Emotion Framework of Rumour Threads on Social Media
Rui Xing, Boyang Sun, Kun Zhang +3
Rumours in online social media pose significant risks to modern society, motivating the need for better understanding of how they develop. We focus specifically on the interface be…
Training and Evaluating with Human Label Variation: An Empirical Study
Kemal Kurniawan, Meladel Mistica, Timothy Baldwin +1
Human label variation (HLV) challenges the standard assumption that a labelled instance has a single ground truth, instead embracing the natural variation in human annotation to tr…
To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
Kemal Kurniawan, Meladel Mistica, Timothy Baldwin +1
This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written b…
Evaluating Evidence Attribution in Generated Fact Checking Explanations
Rui Xing, Timothy Baldwin, Jey Han Lau
Automated fact-checking systems often struggle with trustworthiness, as their generated explanations can include hallucinations. In this work, we explore evidence attribution for f…
Unsupervised Lexical Simplification with Context Augmentation
Takashi Wada, Timothy Baldwin, Jey Han Lau
We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models. Given a target word and its context, our method generat…