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cs.CL2026
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…
cs.CL2024
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…
cs.CL2024
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…