arXiv (All)
open access
DoPR: Reusable Compressed Document Prefixes for Efficient LLM Reranking
Large language models (LLMs) are effective rerankers, but pointwise reranking repeatedly processes the same document across different queries, causing substantial redundant document-side computation. We propose \textbf{DoPR}, a compressed document prefix framework that decouples offline document pro
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
- SumRank: Aligning Summarization Models for Long-Document Listwise Reranking#998158
- AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research#1116803
- Diagnosing LLM Reranker Behavior Under Fixed Evidence Pools#984997
- Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression#1130504
- TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs#1130652
Record · ID 925654
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