Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In light of this, the field of red teaming is undergoing fast-paced growth, highlighting the need for a comprehensive survey covering the entire pipeline and addressing emerging topics. Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models. Additionally, we have developed the “searcher” framework to unify various automatic red teaming approaches. Moreover, our survey covers novel areas including multimodal attacks and defenses, risks around LLM-based agents, overkill of harmless queries, and the balance between harmlessness and helpfulness. Warning: This paper contains examples that may be offensive, harmful, or biased.

Contrastive learning, which learns data representations by contrasting similar and dissimilar instances, has achieved great success in various domains including natural language processing (NLP). Recently, it has been demonstrated that incorporating class labels into contrastive learning, i.e., supervised contrastive learning (SCL), can further enhance the quality of the learned data representations. Although several works have shown empirically that incorporating SCL into classification models leads to better performance, the mechanism of how SCL works for classification is less studied. In this paper, we first investigate how SCL facilitates the classifier learning, where we show that the contrastive region, i.e., the data instances involved in each contrasting operation, has a crucial link to the mechanism of SCL. We reveal that the vanilla SCL is suboptimal since its behavior can be altered by variances in class distributions. Based on this finding, we propose aFocusedContrastiveLoss (FoCL) for classification. Compared with SCL, FoCL defines a finer contrastive region, focusing on the data instances surrounding decision boundaries. We conduct extensive experiments on three NLP tasks: text classification, named entity recognition, and relation extraction. Experimental results show consistent and significant improvements of FoCL over strong baselines on various benchmark datasets, especially in few-shot scenarios.