arXiv Machine Learning

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

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.

arXiv AI

Jun 19

Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

arXiv:2606. 19351v1 Announce Type: cross Abstract: Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support.

By Xinyan Zhu, Yaoqi Liu, Yue Gao, Huadong Ma, Cheng Yang, Chuan Shi

llms nlp benchmarks safety

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.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

arXiv AI

Aug 6

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

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

llms rag nlp benchmarks safety

arXiv AI

Jul 28

KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering

arXiv:2607. 22652v1 Announce Type: new Abstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA).

By Yike Wu, Nan Hu, Guilin Qi, Guohui Xiao, Chen Jiang, Xinchun Zou, Yuchen Lu, Songlin Zhai, Yongrui Chen, Yuyang Zhang, Xiaoguang Li, Lifeng Shang, Jiaoyan Chen, Jeff Z. Pan

llms rag agents nlp safety

arXiv Machine Learning

Jun 11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.

By Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang

llms fine-tuning benchmarks safety

arXiv AI

Jul 21

Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph

arXiv:2607. 17266v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing.

By Peiji Yu, Xin Chen, Tianxing Wu

llms agents nlp benchmarks safety

arXiv AI

Aug 11

KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs

arXiv:2608. 09779v1 Announce Type: cross Abstract: Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts where general-purpose LLMs and RAG tend to underperform.

By Ghanshyam Verma, Simanta Sarkar, Devishree Pillai, Hotaka Shiokawa, Yourong Xu, Fiona Veazey, Peter Hubbert, Hui Su, Paul Buitelaar

llms rag nlp

arXiv AI

Jun 3

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

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

llms benchmarks safety

arXiv AI

Jun 29

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

arXiv:2606. 28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts.

By Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang

nlp

arXiv AI

Aug 11

KGCache: Amortized Subgraph Retrieval for KG Reasoning with LLMs

arXiv:2608. 07954v1 Announce Type: new Abstract: Large language models can answer knowledge-intensive questions more reliably when they are grounded with knowledge graphs, but systems such as Think-on-Graph and Reasoning-on-Graph repeatedly query the same graph neighborhoods across different questions.

By Uros Stanic, Changcheng Yuan, Sabuj Laskar, Ariful Azad

llms nlp

arXiv AI

Jul 9

InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs

arXiv:2607. 07422v1 Announce Type: new Abstract: Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption.

By Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah, Raghava Mutharaju

rag benchmarks

arXiv AI

Aug 5

DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning

arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.

By Le Xiang, Zhicheng Guan, Hong Chen, Xiaocong Lin, Zhenghua Lei, Teng Hu, Bolei He, Long Zeng

llms rag agents nlp fine-tuning multimodal benchmarks

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