  Large language models (LLMs) have revolutionized natural language processing.
However, effectively incorporating complex and potentially noisy user
interaction data remains a challenge. To address this, we propose User-LLM, a
novel framework that leverages user embeddings to contextualize LLMs. These
embeddings, distilled from diverse user interactions using self-supervised
pretraining, capture latent user preferences and their evolution over time. We
integrate these user embeddings with LLMs through cross-attention and
soft-prompting, enabling LLMs to dynamically adapt to user context. Our
comprehensive experiments on MovieLens, Amazon Review, and Google Local Review
datasets demonstrate significant performance gains across various tasks.
Notably, our approach outperforms text-prompt-based contextualization on long
sequence tasks and tasks that require deep user understanding while being
computationally efficient. We further incorporate Perceiver layers to
streamline the integration between user encoders and LLMs, reducing
computational demands.
