arXiv (All)
open access
Retrieval-in-the-Chain: Bootstrapping Large Language Models for Generative Retrieval
Generative retrieval (GR) is an emerging paradigm that leverages large language models (LLMs) to autoregressively generate document identifiers (docids) relevant to a given query. Prior works have focused on leveraging the generative capabilities of LLMs to improve GR, while overlooking that their r
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Amortizing intractable inference in large language models#798540
- Amortizing intractable inference in large language models#797127
- BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval#1063424
- The Real Explanation of GPT: A Structural Decomposition of the Large Language Model Black Box#692186
- Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster#818669
Record · ID 1048303
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive