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This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains.
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Unsupervised Dense Information Retrieval with Contrastive Learning
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Selecting which Dense Retriever to use for Zero-Shot Search
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Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
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A Personalized Dense Retrieval Framework for Unified Information Access. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 121–130
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Memory-Augmented LLM Personalization with Short- and Long-Term Memory Coordination
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