Papers with graph-based reasoning
A Dynamic Self-Evolving Extraction System (2026.acl-demo)
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| Challenge: | High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. |
| Approach: | They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text. |
| Outcome: | The proposed toolkit continuously improves as it is used in medical, legal, and HR domains. |
Reading Comprehension with Graph-based Temporal-Casual Reasoning (C18-1)
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| Challenge: | Existing methods for reading comprehension tasks ignore semantic relations between sentences or use sliding window scanning over the words of the passage without sentence breaks. |
| Approach: | They propose a method to integrate information from multiple sentences to answer complex questions. |
| Outcome: | Experiments on RACE and MCTest show that the proposed approach improves state-of-the-art methods on simple factoid questions. |
MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025.findings-emnlp)
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Nguyen Manh Hieu, Vu Lam Anh, Hung Pham Van, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen
| Challenge: | Recent advances in Graph-based RAG (GRAG) frameworks focus on knowledge graphs for cross-lingual retrieval. |
| Approach: | They propose a new GRAG framework for cross-lingual question answering . MaGiX constructs a multi-granular cross-linguistic knowledge graph using fine-grained attribute descriptions and cross-synonym edges. |
| Outcome: | The proposed framework outperforms prior GRAG systems in retrieval accuracy and generation quality. |
Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)
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| Challenge: | JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency . |
| Approach: | They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning. |
| Outcome: | The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation. |
A Simple Yet Strong Pipeline for HotpotQA (2020.emnlp-main)
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| Challenge: | Existing models for multi-hop question answering have been proposed with varying complexities. |
| Approach: | They propose to use BERT to identify potentially relevant sentences independently of each other . they feed selected sentences into a standard BERT span prediction model to choose an answer . |
| Outcome: | The proposed pipeline outperforms existing models on hotpotQA and support identification. |
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. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents (2025.emnlp-main)
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Manan Suri, Puneet Mathur, Nedim Lipka, Franck Dernoncourt, Ryan A. Rossi, Vivek Gupta, Dinesh Manocha
| Challenge: | Flowcharts are a critical tool for visualizing decision-making processes, but their non-linear structure and complex visual-textual relationships make it difficult to interpret them using LLMs. |
| Approach: | They propose a task of Fine-grained Flowchart Attribution to trace components grounding a flowchart referring LLM response. |
| Outcome: | The proposed agent mitigates visual hallucinations in LLM answers over baselines by 10–14% on a FlowExplainBench dataset. |
TabReX: Tabular Referenceless eXplainable Evaluation (2026.acl-long)
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| Challenge: | Existing metrics for evaluating the quality of tables generated by large language models flatten tables into text, ignoring structure or relying on fixed references that limit generalization. |
| Approach: | They propose a reference-less framework for evaluating tabular generation via graph-based reasoning . tabReX converts source text and generated tables into canonical knowledge graphs . |
| Outcome: | The proposed framework provides a high correlation with expert rankings and stable under harder perturbations. |