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.
By Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu
arXiv:2604. 12503v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios.
By Shuai Wang, Xixi Wang, Yinan Yu
The paper introduces a new entity alignment foundation model that overcomes the limitations of existing models by addressing the "reasoning horizon gap". It employs a parallel encoding strategy that uses seed entity pairs as local anchors to guide message passing, thereby shortening inference paths and improving alignment across sparse, heterogeneous knowledge graphs. The model also incorporates a merged relation graph and a learnable interaction module, and experimental results demonstrate its strong generalizability to unseen knowledge graphs.
By Yuanning Cui, Zequn Sun, Wei Hu, Kexuan Xin, Zhangjie Fu
arXiv:2606. 03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.
By Weiwei Ding, Zixuan Li, Long Bai, Zhuo Chen, Kun Su, Fei Wang, Xiaolong Jin, Jin Zhang, Jiafeng Guo, Xueqi Cheng
arXiv:2608.22762v1 Announce Type: new
Abstract: Knowledge graph question answering (KGQA) is a key task for evaluating KG-augmented Large Language Models (LLMs), and complex KGQA that requires multi-...
By Chenhui Liu, Jianpeng Zhou, Jiahai Wang
arXiv:2502. 11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing.
By Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing Qin