Academic

Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning

arXiv:2603.12290v1 Announce Type: cross Abstract: Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Current miscitation detection methods, which primarily rely on semantic similarity or network anomalies, struggle to capture the nuanced relationship between a citation's context and its place in the wider network. While large language models (LLMs) offer powerful capabilities in semantic reasoning for this task, their deployment is hindered by hallucination risks and high computational costs. In this work, we introduce LLM-Augmented Graph Learning-based Miscitation Detector (LAGMiD), a novel framework that leverages LLMs for deep semantic reasoning over citation graphs and distills this knowledge into graph neural networks (GNNs) for efficient and scalable miscitation detection. Specifically, LAGMiD introduces an evidence-

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Huidong Wu, Haojia Xiang, Jingtong Gao, Xiangyu Zhao, Dengsheng Wu, Jianping Li · · 1 min read · 10 views
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arXiv:2603.12290v1 Announce Type: cross Abstract: Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Current miscitation detection methods, which primarily rely on semantic similarity or network anomalies, struggle to capture the nuanced relationship between a citation's context and its place in the wider network. While large language models (LLMs) offer powerful capabilities in semantic reasoning for this task, their deployment is hindered by hallucination risks and high computational costs. In this work, we introduce LLM-Augmented Graph Learning-based Miscitation Detector (LAGMiD), a novel framework that leverages LLMs for deep semantic reasoning over citation graphs and distills this knowledge into graph neural networks (GNNs) for efficient and scalable miscitation detection. Specifically, LAGMiD introduces an evidence-chain reasoning mechanism, which uses chain-of-thought prompting, to perform multi-hop citation tracing and assess semantic fidelity. To reduce LLM inference costs, we design a knowledge distillation method aligning GNN embeddings with intermediate LLM reasoning states. A collaborative learning strategy further routes complex cases to the LLM while optimizing the GNN for structure-based generalization. Experiments on three real-world benchmarks show that LAGMiD achieves state-of-the-art miscitation detection with significantly reduced inference cost.

Executive Summary

This article presents a novel approach to detecting miscitation on the scholarly web, leveraging large language models (LLMs) for deep semantic reasoning and graph neural networks (GNNs) for efficient and scalable detection. The proposed framework, LLAGMiD, incorporates an evidence-chain reasoning mechanism and a knowledge distillation method to reduce LLM inference costs. Experimental results on three real-world benchmarks demonstrate state-of-the-art miscitation detection with significantly reduced inference cost. The approach addresses the limitations of existing methods, which struggle to capture nuanced relationships between citations and their context. This work has significant implications for the academic community, as accurate detection of miscitation is crucial for maintaining the integrity of scholarly knowledge.

Key Points

  • ▸ LLAGMiD introduces a novel framework for miscitation detection that leverages LLMs for deep semantic reasoning and GNNs for efficient detection
  • ▸ The framework incorporates an evidence-chain reasoning mechanism and a knowledge distillation method to reduce LLM inference costs
  • ▸ Experimental results demonstrate state-of-the-art miscitation detection on three real-world benchmarks with significantly reduced inference cost

Merits

Strength in LLM-Augmentation

LLAGMiD's use of LLMs for deep semantic reasoning significantly improves miscitation detection accuracy, addressing the limitations of existing methods.

Efficient and Scalable Detection

The framework's use of GNNs enables efficient and scalable miscitation detection, making it a practical solution for large-scale academic networks.

Demerits

Hallucination Risks

The reliance on LLMs introduces hallucination risks, which can lead to inaccurate detection if not properly addressed.

High Computational Costs

The deployment of LLMs is hindered by high computational costs, which can be a significant limitation for large-scale applications.

Expert Commentary

The article presents a well-structured and well-executed approach to miscitation detection, leveraging the strengths of LLMs and GNNs. However, the limitations of the approach, including hallucination risks and high computational costs, need to be carefully addressed. The experimental results demonstrate the effectiveness of the framework, but further evaluation on larger and more diverse datasets is necessary to establish its robustness. Additionally, the potential applications of LLAGMiD extend beyond academic networks, and its use in real-world scenarios should be explored.

Recommendations

  • ✓ Further evaluation of LLAGMiD on larger and more diverse datasets to establish its robustness and generalizability.
  • ✓ Investigation of the framework's potential applications in real-world scenarios, such as fake news detection and academic integrity management.

Sources

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