arXiv Machine Learning
Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 2
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
KGFR introduces a Knowledge Graph Foundation Retriever that collaborates with large language models to enhance knowledge‑intensive question answering. By encoding relations with LLM‑generated descriptions and initializing entities from question roles, KGFR enables zero‑shot generalization to unseen knowledge graphs. Its Asymmetric Progressive Propagation technique efficiently handles large graphs, while a controllable reasoning loop allows the LLM to request candidate answers, supporting facts, and reasoning paths.
arXiv Computation and Language
Oct 5
Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching
Enrich-on-Graph (EoG) is a flexible framework that uses large language models to enrich knowledge graphs, thereby bridging the semantic gap between structured graphs and unstructured queries in complex reasoning tasks. By leveraging LLMs’ prior knowledge, EoG enables efficient evidence extraction from knowledge graphs, achieving precise and robust reasoning while maintaining low computational costs and scalability. The authors also introduce three graph quality evaluation metrics for query‑graph alignment, theoretically validate their optimization objectives, and demonstrate state‑of‑the‑art performance on two KGQA benchmark datasets.
arXiv AI
Oct 27
UniGFM-RAG: Unify Graph Foundation Models and Large Language Models for End-to-End Retrieval and Reasoning
arXiv:2610.34866v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown remarkable capabilities across tasks, but they still struggle with complex reasoning that requires integrating...
arXiv Machine Learning
Sep 30
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
arXiv Computation and Language
Oct 27
Towards a Learnable Retriever for Textual Graph Question Answering
arXiv:2610.33511v1 Announce Type: new Abstract: Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to groun...
arXiv AI
Jun 6
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.