The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them. In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.

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Neural Information Processing Systems http://nips.cc/

Our key insight is that the user's examination and click behavior

However, many previous works studied textual and relational retrieval tasks as separate topics.

An efficient two-staged approach is designed for exploring CSPG, with fast approaching and cross-partition expansion.