most citedComprehensive Assessment of Toxicity in ChatGPT

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

MoQE: Improve Quantization Model performance via Mixture of Quantization Experts

Jinhao Zhang, Yunquan Zhang, Boyang Zhang +2

Quantization method plays a crucial role in improving model efficiency and reducing deployment costs, enabling the widespread application of deep learning models on resource-constr…

cs.CE2025

A Unified Data-Driven Framework for Efficient Scientific Discovery

Tingxiong Xiao, Xinxin Song, Ziqian Wang +2

Scientific discovery drives progress across disciplines, from fundamental physics to industrial applications. However, identifying physical laws automatically from gathered dataset…

hep-th20251 cited

Bootstrapping the Cosmological Collider with Resonant Features

Dong-Gang Wang, Bowei Zhang

Signatures of heavy particles during inflation are exponentially suppressed by the Boltzmann factor when the masses are far above the Hubble scale. In more realistic scenarios, how…

cs.CY20235 cited

Comprehensive Assessment of Toxicity in ChatGPT

Boyang Zhang, Xinyue Shen, Wai Man Si +6

Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP. The emerging large language models (LLMs), su…

cs.CR20231 cited

A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

Boyang Zhang, Xinlei He, Yun Shen +2

Building advanced machine learning (ML) models requires expert knowledge and many trials to discover the best architecture and hyperparameter settings. Previous work demonstrates t…