Fact Verification on Knowledge Graph via Programmatic Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for fact verification on knowledge graphs use implicit reasoning to predict entailment between claims and KG triples. |
| Approach: | They propose a framework that integrates large language models for fact verification on knowledge graphs. |
| Outcome: | The proposed framework outperforms existing methods on knowledge graphs with 86.82% accuracy. |
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
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| Challenge: | Existing verification methods rely on unstructured text corpora to break down claims . despite strong reasoning abilities, modern LLMs struggle with modular pipelines . |
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| Challenge: | Existing methods for factual reasoning over knowledge graphs lack support for multiple quantifiers and connectives. |
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| Challenge: | Recent advances in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks. |
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| Challenge: | Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. |
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Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
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GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
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Systematic Assessment of Factual Knowledge in Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing question-answering benchmarks for large language models have limitations regarding factual knowledge coverage, as they focus on generic domains and overlap with pretraining data. |
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