A new paper on arXiv introduces Internalizer, a method for mapping a context directly to a LoRA adapter using a hypernetwork. This lets a large language model carry that context in its weights rather than in its input or memory, potentially making the context portable and reusable across tasks.
The authors note that prior work in this direction has only been demonstrated on base models up to 14 billion parameters. Internalizer is presented as a way to extend this approach to very large language models, which is significant because hypernetwork-based adapter generation becomes more challenging as the base model grows.
Because the abstract is truncated, the specific techniques and evaluation results are not available from the source. The paper's contribution, as stated, is the scaling of context-to-parameter mapping to much larger models, but the details of how that is achieved remain outside the provided text.