C laim PKG : Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM (2025)

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, 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-the-art 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.

## introduction in today's rapidly evolving information landscape, distinguishing fact from misinformation is becoming more challenging, especially with the rise of ai-generated content. robust claim verification systems, leveraging nlp methods to automatically assess the veracity of claims @xcite , are essential to ensure information reliability. effective methods require not only accuracy but also transparency, necessitating strong reasoning to identify evidence and provide clear justifications @xcite . most existing verification approaches focus on unstructured text corpora, using techniques like chain-of-thought (cot) reasoning @xcite to break down claims for verification. approaches like programfc @xcite and folk @xcite employ modular pipelines to verify claims against text-based knowledge bases (figure 1(a) ). however, the inherent limitations of text representation pose challenges. specifically, ambiguous entity references and complex multihop relationships make it difficult to perform rigorous verification against unstructured text. in contrast, knowledge graphs (kgs) provide structured relationships for effective reasoning @xcite ), yet their use in claim verification remains limited. existing kgbased approaches (figure 1 (b)) @xcite @xcite lack end-toend solutions, often requiring pre-extracted entities via modules like entity or relation extraction. meanwhile, despite excelling at general reasoning, llms struggle with kg-specific tasks like entity resolution and multi-hop reasoning @xcite , suggesting the need for a system combining llm capabilities with kg-based inference. overall, solving claim verification problems is hindered by following major limitations: (1) entity ambiguity: systems must accurately disambiguate entities within claims to identify relevant evidence @xcite ; (2) multihop reasoning: complex claims often require reasoning across multiple evidence from different sources @xcite ; and (3) limited integration of kgs and llms: current approaches are underexploring the potential of combining the application of structured representation with strong inference capabilities of llms @xcite . to address these challenges, we propose claimpkg (claim verification using pseudo-subgraph in knowledge graphs), a novel endto-end framework that synergizes the adaptability and generalization strengths of llms with the structured and rigorous representation of kgs to enable robust and transparent claim verification. as specified in figure 1(c ), claimpkg operates through three phases: (1) pseudo-subgraphs generation: a kg-specialized lightweight llm generates pseudo subgraphs as the representations of input claims under a trie-based kg-entity constraint, ensuring the correctness of extracted entities; (2) subgraphs retrieval: a retrieval algorithm considers generated pseudo subgraphs as queries to identify actual relevant kg subgraphs as evidence; and (3) general reasoning: a generalpurpose llm reasons over the retrieved kg subgraphs to produce the verdict and human-readable justifications. through extensive experiments on the factkg dataset, claimpkg achieves state-ofthe-art performance, demonstrating its effectiveness over various claim types with a small number of training samples. furthermore, its zero-shot generalizability to unstructured datasets (hover, feverous) highlights its robustness. our contributions can be summarized as follows: (1) we introduce claimpkg, a holistic framework that integrates llms and kgs for accurate and interpretable claim verification, handling various types of claims in a unified manner; (2) we develop a lightweight specialized llm with its according decoding algorithm for pseudo-subgraph generation and pair it with general-purpose llms to achieve robust reasoning; and (3) we validate the effectiveness of claimpkg through extensive experiments, achieving state-of-the-art performance on structure-based datasets and generalizing to unstructure-based datasets. ## preliminary knowledge graph: knowledge graph (kg) g represents facts as triplets of format @xmath0, r, e ′ ), where entities e, e ′ ∈ e are connected by a relation @xmath1; r can also be referred as r(e, e ′ ). claim verification: given a claim c, a verification model f determines its veracity as supported or refuted based on an external knowledge base k, while also providing a justification j to explain the predicted label. this work specifically considers the scenario where k is structured as a knowledge graph g, enabling reasoning over graph knowledge to infer v and j. formally, the verification process is defined as: (v, j) = f(c, g). trie-based constrained decoding: a trie @xcite indexes predefined token sequences, where each root-to-node path represents a prefix. during llm generation, this structure restricts token selection to only valid trie paths, ensuring reliable output. ## conclusion in this work, we present claimpkg, a novel claim verification combining the structure of knowledge graphs with the adaptability and reasoning of large language models. through pseudosubgraph generation, subgraph retrieval, and general reasoning, it addresses limitations while ensuring transparency. extensive experiments show state-of-the-art performance and generalizability across datasets, making claimpkg a step toward reliable and explainable misinformation detection.