ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM
Year: 2025
Type: conference-paper
Abstract: Integrating knowledge graphs (KGs) to enhance the reasoning capabilities of large language models (LLMs) is an emerging research challenge in claim verification.While KGs provide structured, semantically rich representations well-suited for reasoning, most existing verification methods rely on unstructured text corpora, limiting their ability to effectively leverage KGs.Additionally, despite possessing strong reasoning abilities, modern LLMs struggle with multi-step modular pipelines and reasoning over KGs without adaptation.To address these challenges, we propose ClaimPKG 1 , an end-to-end framework that seamlessly integrates LLM reasoning with structured knowledge from KGs.Specifically, the main idea of ClaimPKG is to employ a lightweight, specialized LLM to represent the input claim as pseudo-subgraphs, guiding a dedicated subgraph retrieval module to identify relevant KG subgraphs.These retrieved subgraphs are then processed by a general-purpose LLM to produce the final verdict and justification.Extensive experiments on the FactKG dataset demonstrate that ClaimPKG achieves state-of-theart performance, outperforming strong baselines in this research field by 9%-12% accuracy points across multiple categories.Furthermore, ClaimPKG exhibits zero-shot generalizability to unstructured datasets such as HoVer and FEVEROUS, effectively combining structured knowledge from KGs with LLM reasoning across various LLM backbones.
Authors: Hoang Pham, Thanh-Do Nguyen, Khac-Hoai Nam Bui
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888665