https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs/Head https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs/assertion https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs/provenance https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs/pubinfo https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RA8FdvpE7WFxgl-MQWsvkngyULSqpp0x0V8S5FUCz9Wbs/assertion https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/dc/terms/title KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#KGCraft https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck https://doi.org/10.48550/arXiv.2601.19447 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#IKA https://doi.org/10.48550/arXiv.2601.19447 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/ns/prov#Entity https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA http://www.w3.org/2000/01/rdf-schema#comment CO-GAT (ELECTRA) applies graph attention over scientific evidence, with ELECTRA (a pre-trained language model) as its encoder. This method enhances the LLM's understanding and reasoning by integrating graph structures into its evidence processing during inference. https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA http://www.w3.org/2000/01/rdf-schema#label CO-GAT (ELECTRA) https://neverblink.eu/ontologies/llm-kg/methods#COGATELECTRA https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA http://www.w3.org/2000/01/rdf-schema#comment FactL-LaMA is an LLM-based method that enhances fact-checking by augmenting LLMs with external knowledge sources during inference. This external knowledge (often KG-based in this context) helps improve the LLM's accuracy in verification tasks. https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA http://www.w3.org/2000/01/rdf-schema#label FactL-LaMA https://neverblink.eu/ontologies/llm-kg/methods#FactLLaMA https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck http://www.w3.org/2000/01/rdf-schema#comment GraphCheck is an LLM-based verifier that incorporates lightweight graph signals alongside instruction-style prompting. The graph signals are used during the LLM's inference to enhance its verification capabilities by providing structured insights. https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck http://www.w3.org/2000/01/rdf-schema#label GraphCheck https://neverblink.eu/ontologies/llm-kg/methods#GraphCheck https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM https://neverblink.eu/ontologies/llm-kg/methods#IKA http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#IKA http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#IKA http://www.w3.org/2000/01/rdf-schema#comment IKA is a method that uses example graphs (Knowledge Graphs) to enhance LLM capabilities for claim verification and explanation. The KGs provide structured context during the LLM's inference to improve its fact-checking performance. https://neverblink.eu/ontologies/llm-kg/methods#IKA http://www.w3.org/2000/01/rdf-schema#label IKA https://neverblink.eu/ontologies/llm-kg/methods#IKA https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM https://neverblink.eu/ontologies/llm-kg/methods#KGCraft http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#KGCraft http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#KGCraft http://www.w3.org/2000/01/rdf-schema#comment KG-CRAFT is a novel method that enhances LLM capabilities for automated fact-checking. It leverages KGs by first using LLMs to construct a KG from claims and reports. 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