arXiv (All)
open access
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely on finetuned LLMs or GNN retrievers are limited by dataset-specific tuning and scalability on large or unseen graphs. W
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
- Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering#1034218
- Automated Generation of Massive Reasonable Empirical Theorems by Forward Reasoning Based on Strong Relevant Logics - A Solution to the Problem of LLM Pre-training Data Exhaustion#689385
- Rewarding Novel Deductions: Solver-guided Process Rewards for Logical Reasoning#1128919
- The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs#997136
- MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering#798736
Record · ID 820276
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