arXiv Machine Learning By Albert Sawczyn, Piotr Bielak, Tomasz Kajdanowicz

KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering

Read the original on arXiv Machine Learning →

arXiv:2606. 00328v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-graph subgraph.

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arXiv AI
Sep 15

FedV-KGQA in Practice: Design Lessons and an Interactive Prototype

FedV-KGQA addresses multi‑hop question answering over vertically partitioned knowledge graphs where each silo holds disjoint relation types. The system trains local embeddings, concatenates silo‑specific entity views, anchors questions at a topic entity, and ranks candidates without sharing raw triples. Experiments show federated fusion nearly matches centralized accuracy, that anchoring and enrichment are more critical than embedding choice, and that the cheapest encoder depends on target accuracy.

By Md Saikat Islam Khan Bappy, Oshani Seneviratne