4 citations · 6 across the 5 of their papers we have counts for
5 papers
In-Context Learning Can Re-learn Forbidden Tasks
Sophie Xhonneux, David Dobre, Jian Tang +2
Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vulnerabilities. One perspective on LLM safe…
GraphText: Graph Reasoning in Text Space
Jianan Zhao, Le Zhuo, Yikang Shen +5
Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite th…
Less is More: Learning Prominent and Diverse Topics for Data Summarization
Jian Tang, Cheng Li, Ming Zhang +1
Statistical topic models efficiently facilitate the exploration of large-scale data sets. Many models have been developed and broadly used to summarize the semantic structure in ne…
Identity-sensitive Word Embedding through Heterogeneous Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings o…
"Look Ma, No Hands!" A Parameter-Free Topic Model
Jian Tang, Ming Zhang, Qiaozhu Mei
It has always been a burden to the users of statistical topic models to predetermine the right number of topics, which is a key parameter of most topic models. Conventionally, auto…