69 citations · 127 across the 30 of their papers we have counts for
8 papers · 1 filter
FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
Peng Wang, Biyu Zhou, Xuehai Tang +2
Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access. To address t…
Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers
Liang Lin, Zhihao Xu, Xuehai Tang +5
The safety of large language models (LLMs) has garnered significant research attention. In this paper, we argue that previous empirical studies demonstrate LLMs exhibit a propensit…
Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs
Xikang Yang, Biyu Zhou, Xuehai Tang +2
Large Language Models (LLMs) demonstrate impressive capabilities across a wide range of tasks, yet their safety mechanisms remain susceptible to adversarial attacks that exploit co…
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing
Peng Wang, Biyu Zhou, Xuehai Tang +2
Large Language Models often contain factually incorrect or outdated knowledge, giving rise to model editing methods for precise knowledge updates. However, current mainstream locat…
Chain of Attack: a Semantic-Driven Contextual Multi-Turn attacker for LLM
Xikang Yang, Xuehai Tang, Songlin Hu +1
Large language models (LLMs) have achieved remarkable performance in various natural language processing tasks, especially in dialogue systems. However, LLM may also pose security…
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
Xing Wu, Chaochen Gao, Zijia Lin +3
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Liangjun Zang +3
Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (…
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Yipeng Su +3
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding. Among the previous un-supervised methods, the latest state-of-the-art method,…