Watermarks for Language Models via Probabilistic Automata

A new study introduces STA-Attention, a framework utilizing Top-K Sparse Autoencoders to analyze the Key-Value (KV) cache in long-context Large Language Models (LLMs). This research reveals a Key-Value Asymmetry, where Key vectors act as sparse routers while Value vectors contain dense content, leading to a proposed Dual-Budget Strategy for optimizing semantic component retention.

A recent study highlights the importance of safety alignment in large language models (LLMs) as they are increasingly adapted for various tasks. The research identifies safety degradation during fine-tuning, attributing it to catastrophic forgetting, and proposes continual learning (CL) strategies to preserve safety. The evaluation of these strategies shows that they can effectively reduce attack success rates compared to standard fine-tuning methods.