Selected work

Representative Papers

POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment

Aug 17, 2026

This study addresses the cold-start challenge caused by interaction sparsity in POI recommendation by proposing the LLM-MGCL framework. This approach integrates large language model semantics with geospatial information to construct auxiliary graphs, employing bidirectional contrastive learning to align behavioral, semantic, and spatial views. Such alignment effectively compensates for missing collaborative filtering signals and enhances graph neural network representations. Evaluations on the Yelp dataset demonstrate that LLM-MGCL outperforms LightGCN, achieving improvements of 52.0% in Recall@20 and 64.8% in NDCG@20. These results significantly surpass traditional baselines, validating the effectiveness of combining multimodal knowledge augmentation with contrastive learning to mitigate cold-start issues in location-based recommendation systems.

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Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

Apr 17, 2026

This study addresses the challenge of limited interpretability in machine learning models within manufacturing contexts, where opaque predictions often hinder effective decision-making. To bridge this gap, the authors propose a novel paradigm that leverages large language models to dynamically retrieve relevant triples from domain-specific knowledge graphs, thereby structurally linking expert knowledge with model predictions and generating user-friendly natural language explanations. Integrating knowledge graphs, large language models, and explainable artificial intelligence (XAI), the approach was evaluated on 33 manufacturing-related tasks. Results demonstrate superior performance across both quantitative metrics—such as accuracy and consistency—and qualitative dimensions, including clarity and practical utility, significantly enhancing model interpretability and decision support capabilities in real-world industrial settings.

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Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models

Mar 23, 2026

This study addresses the lack of systematic evaluation regarding the reliability and alignment with human judgment of large language models (LLMs) when deployed as automated evaluators. The authors construct a human-annotated gold-standard dataset spanning eight distinct tasks and conduct the first large-scale empirical analysis of 37 open- and closed-source conversational LLMs under five裁判 prompting strategies, a two-stage judging mechanism, and task-specific fine-tuning. Results demonstrate that GPT-4o, open-source models with at least 32 billion parameters, and Qwen2.5-14B achieve high agreement with human judgments when paired with appropriate prompts, thereby validating the feasibility of using LLMs as reliable automated evaluators. The findings offer empirical guidance for prompt design, model selection, and architectural optimization in automated assessment systems.

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Recent publications

Latest Papers

POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment

Aug 17, 2026

This study addresses the cold-start challenge caused by interaction sparsity in POI recommendation by proposing the LLM-MGCL framework. This approach integrates large language model semantics with geospatial information to construct auxiliary graphs, employing bidirectional contrastive learning to align behavioral, semantic, and spatial views. Such alignment effectively compensates for missing collaborative filtering signals and enhances graph neural network representations. Evaluations on the Yelp dataset demonstrate that LLM-MGCL outperforms LightGCN, achieving improvements of 52.0% in Recall@20 and 64.8% in NDCG@20. These results significantly surpass traditional baselines, validating the effectiveness of combining multimodal knowledge augmentation with contrastive learning to mitigate cold-start issues in location-based recommendation systems.

0 citationsRead paper

Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

Apr 17, 2026

This study addresses the challenge of limited interpretability in machine learning models within manufacturing contexts, where opaque predictions often hinder effective decision-making. To bridge this gap, the authors propose a novel paradigm that leverages large language models to dynamically retrieve relevant triples from domain-specific knowledge graphs, thereby structurally linking expert knowledge with model predictions and generating user-friendly natural language explanations. Integrating knowledge graphs, large language models, and explainable artificial intelligence (XAI), the approach was evaluated on 33 manufacturing-related tasks. Results demonstrate superior performance across both quantitative metrics—such as accuracy and consistency—and qualitative dimensions, including clarity and practical utility, significantly enhancing model interpretability and decision support capabilities in real-world industrial settings.

0 citationsRead paper

Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models

Mar 23, 2026

This study addresses the lack of systematic evaluation regarding the reliability and alignment with human judgment of large language models (LLMs) when deployed as automated evaluators. The authors construct a human-annotated gold-standard dataset spanning eight distinct tasks and conduct the first large-scale empirical analysis of 37 open- and closed-source conversational LLMs under five裁判 prompting strategies, a two-stage judging mechanism, and task-specific fine-tuning. Results demonstrate that GPT-4o, open-source models with at least 32 billion parameters, and Qwen2.5-14B achieve high agreement with human judgments when paired with appropriate prompts, thereby validating the feasibility of using LLMs as reliable automated evaluators. The findings offer empirical guidance for prompt design, model selection, and architectural optimization in automated assessment systems.

0 citationsRead paper