Papers by Emiliano Penaloza

1 papers
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)

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Challenge: Existing approaches for personalizing large language models require modifying parameters.
Approach: They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue .
Outcome: The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks.

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