E2P projects pre-computed user embeddings into a single soft prefix token for frozen LLMs, reporting gains on four personalization tasks, though its reproduction scripts write zero embeddings.
Transparent, scrutable and explainable user models for personalized recommendation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models
E2P projects pre-computed user embeddings into a single soft prefix token for frozen LLMs, reporting gains on four personalization tasks, though its reproduction scripts write zero embeddings.