6 papers
Understanding Evaluation Illusion in Diffusion Large Language Models
Hengxiang Zhang, Jiaxi Ren, Renchunzi Xie +1
Despite the capability of parallel decoding, diffusion large language models (dLLMs) require many denoising steps to maintain generation quality, motivating recent research on effi…
Toward Early Quality Assessment of Text-to-Image Diffusion Models
Huanlei Guo, Hongxin Wei, Bingyi Jing
Recent text-to-image (T2I) diffusion and flow-matching models can produce highly realistic images from natural language prompts. In practical scenarios, T2I systems are often run i…
Natural Language-Driven Global Mapping of Martian Landforms
Yiran Wang, Shuoyuan Wang, Zhaoran Wei +7
Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch…
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
Hengxiang Zhang, Hongfu Gao, Qiang Hu +7
With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous w…
Fine-tuning can Help Detect Pretraining Data from Large Language Models
Hengxiang Zhang, Songxin Zhang, Bingyi Jing +1
In the era of large language models (LLMs), detecting pretraining data has been increasingly important due to concerns about fair evaluation and ethical risks. Current methods diff…
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
Qiang Hu, Hengxiang Zhang, Hongxin Wei
Over-parameterized models are typically vulnerable to membership inference attacks, which aim to determine whether a specific sample is included in the training of a given model. P…