3 citations · 6 across the 5 of their papers we have counts for
6 papers
Beyond Local Edits: Embedding-Virtualized Knowledge for Broader Evaluation and Preservation of Model Editing
Shuainan Liu, Xuanang Chen, Ben He +1
Knowledge editing methods for large language models are commonly evaluated using predefined benchmarks that assess edited facts together with a limited set of related or neighborin…
Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
Kevin Robbins, Xiaotong Liu, Yu Wu +4
Vision-Language Models like CLIP create aligned embedding spaces for text and images, making it possible for anyone to build a visual classifier by simply naming the classes they w…
CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
Duo Wu, Jinghe Wang, Yuan Meng +3
Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g.…
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
Ying Zhou, Xinyao Wang, Yulei Niu +6
Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for h…
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
Ying Zhou, Ben He, Le Sun
With the development of large language models (LLMs), detecting whether text is generated by a machine becomes increasingly challenging in the face of malicious use cases like the…
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
Xinlin Peng, Ying Zhou, Ben He +2
Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks. However, the utilization of these models carries inherent risks, including but not lim…