However, practical model deployment often operates in the gap between these two limit cases, as real-world applications demand adaptation to specific subdomains, tasks or concepts -- spread over the entire, varying life cycle of a model.

However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention.

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.

We collect 312 programs from various sources, including daily programs from college homework, the international competition (SV -COMP), benchmarks from previous papers (SLING), and programs from real-world software systems (Linux Kernel, GlibC, LiteOS, and Zephyr).

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

Surgical video-language pretraining (VLP) faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data.

In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games .