4 papers
NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning
Jiayu Xu, Junbiao Pang
Learning from Noisy Labels (LNL) remains a fundamental challenge in deep learning because real-world datasets often contain corrupted annotations. Most existing methods rely on lab…
Estimating Exam Item Difficulty with LLMs: A Benchmark on Brazil's ENEM Corpus
Thiago Brant, Julien Kühn, Jun Pang
As Large Language Models (LLMs) are increasingly deployed to generate educational content, a critical safety question arises: can these models reliably estimate the difficulty of t…
Malicious Repurposing of Open Science Artefacts by Using Large Language Models
Zahra Hashemi, Zhiqiang Zhong, Jun Pang +1
The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring LLMs that autonomously generat…
Evaluating Multi-Agent Defences Against Jailbreaking Attacks on Large Language Models
Maria Carolina Cornelia Wit, Jun Pang
Recent advances in large language models (LLMs) have raised concerns about jailbreaking attacks, i.e., prompts that bypass safety mechanisms. This paper investigates the use of mul…