5 papers
Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness
Haoting Qian, Qingjie Zhang, Zhicong Huang +2
Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not. Current unlearning benchmarks inc…
Speculating LLMs' Chinese Training Data Pollution from Their Tokens
Qingjie Zhang, Di Wang, Haoting Qian +7
Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-min…
Understanding the Dilemma of Unlearning for Large Language Models
Qingjie Zhang, Haoting Qian, Zhicong Huang +5
Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…
Advancing Compositional LLM Reasoning with Structured Task Relations in Interactive Multimodal Communications
Xinye Cao, Hongcan Guo, Guoshun Nan +9
Interactive multimodal applications (IMAs), such as route planning in the Internet of Vehicles, enrich users' personalized experiences by integrating various forms of data over wir…
Understanding the Dark Side of LLMs' Intrinsic Self-Correction
Qingjie Zhang, Di Wang, Haoting Qian +7
Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic se…