Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined.

TKGs, TKG representation learning poses great challenges to the research community.

Jailbreak attacks cause large language models (LLMs) to generate harmful, unethical, or otherwise objectionable content. Evaluating these attacks presents a number of challenges, which the current collection of benchmarks and evaluation techniques do not adequately address.

HEST -1k processing enabled the identification of 2.1 million expression-morphology pairs and over 76 million nuclei.

In this paper, we introduce a novel class of backdoors in transformer models, that, in contrast to prior art, are unelicitable in nature.