2026
Learning to Recall with Transformers Beyond Orthogonal Embeddings
ICLR 2026poster
Modern large language models (LLMs) excel at tasks that require storing and retrieving knowledge, such as factual recall and question answering. Transformers are central to this capability, thanks to their ability to encode information during training and retrieve it at inference. Existing theoretic…