5 accepted papers
Ensuring large language models (LLM) behave consistently with human goals, values, and intentions is crucial for their safety but yet computationally expensive. To reduce the computational cost of alignment training of LLMs, especially for those with a huge number of parameters, and to reutilize lea…
Prior research has revealed that certain abstract concepts are linearly represented as directions in the representation space of LLMs, predominantly centered around English. In this paper, we extend this investigation to a multilingual context, with a specific focus on human values-related concepts…
It is widely acknowledged that large language models (LLMs) encode a vast reservoir of knowledge after being trained on mass data. Recent studies disclose knowledge conflicts in LLM generation, wherein outdated or incorrect parametric knowledge (i.e., encoded knowledge) contradicts new knowledge pro…
Protecting privacy leakage in large language models remains a paramount challenge. In this paper, we reveal Privacy Seesaw in LLM privacy safeguarding, a phenomenon where measures to secure specific private information inadvertently heighten exposure risks for other privacy. Through comprehensive an…
Pretrained language models have learned a vast amount of human knowledge from large-scale corpora, but their powerful memorization capability also brings the risk of data leakage. Some risks may only be discovered after the model training is completed, such as the model memorizing a specific phone n…