4 papers
Stealthy Dual-Trigger Backdoors: Attacking Prompt Tuning in LM-Empowered Graph Foundation Models
Xiaoyu Xue, Yuni Lai, Chenxi Huang +4
The emergence of graph foundation models (GFMs), particularly those incorporating language models (LMs), has revolutionized graph learning and demonstrated remarkable performance o…
SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning
Yue Xin, Chen Shen, Shaotian Yan +5
Chain-of-Thought (CoT) prompting enhances the math reasoning capability of large language models (LLMs) to a large margin. However, the mechanism underlying such improvements remai…
Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning
Chenxi Huang, Shaotian Yan, Liang Xie +6
Representation Fine-tuning (ReFT), a recently proposed Parameter-Efficient Fine-Tuning (PEFT) method, has attracted widespread attention for significantly improving parameter effic…
Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
Sinan Fan, Liang Xie, Chen Shen +7
Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our inv…