29 citations · 56 across the 18 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.CY2026
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…
cs.CL2026
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…
cs.LG2026
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…