Papers with citation networks
Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) can help in citation generation but can also amplify existing biases, such as the Matthew effect, and introduce new ones, potentially skewing scientific knowledge dissemination. |
| Approach: | They propose to use large language models to generate scholarly references for in-text citations in papers published after GPT-4's knowledge cut-off date. |
| Outcome: | The proposed model can generate scholarly references for in-text citations, but without the aid of web browsing or a search engine, the results show a similarity between human and LLM citation patterns, but with a more pronounced high citation bias. |
Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)
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| Challenge: | Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources. |
| Approach: | They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models . |
| Outcome: | The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models. |
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context (2026.acl-long)
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| Challenge: | Existing explanation methods for graph neural networks struggle to generate interpretable, fine-grained rationales. |
| Approach: | They propose a lightweight framework that uses large language models to generate interpretable explanations for GNNs. |
| Outcome: | The proposed framework generates interpretable explanations for GNN predictions using large language models. |