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.

To address the length limit issue, the most straightforward method is to simply scale up the input context length.

The resulting Hummingbird model, suitably prompted, performs various scene understanding tasks without modification while approaching the performance of specialists that have been finetuned for each task.

Knowing how the classifiers perform on unseen OOD data remains as a challenging task, owing tothetrain-test distribution divergence [3].

However, it is still very challenging for machines to achievereasonable forecasting.

Dermnet-S chooses the top-30 classes for meta-training.