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Representative Papers

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish

Feb 27, 2025International Conference on Language Resources and Evaluation

Aspect-Sentiment-Opinion Triplet Extraction (ASTE) lacks annotated resources for Slavic languages, particularly Polish, which has no publicly available dataset. Method: We introduce the first Polish ASTE dataset, covering two domains—hotels and e-commerce—and strictly adhering to the standard English ASTE format to ensure cross-lingual comparability. The dataset is manually annotated with fine-grained sentiment structures and released under a CC-BY-NC license. Contribution/Results: Using this resource, we conduct the first systematic evaluation of two mainstream ASTE paradigms and two Polish large language models, revealing critical performance bottlenecks of existing methods on Slavic languages. This work fills a key gap in low-resource, fine-grained sentiment analysis for Slavic languages and establishes a benchmark dataset and empirical foundation for future multilingual ASTE research and model development.

2 citationsRead paper

Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA

Sep 02, 2026

研究探讨了自监督预训练对分子图神经网络的影响，采用LeJEPA方法调整分子图编码器，虽然提高了表示学习但未能稳定提升微调效果。

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Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery

Feb 22, 2025

Neural causal discovery methods face fundamental limitations: they struggle to reliably distinguish true from spurious causal edges under finite samples, and the faithfulness assumption—critical for identifiability—frequently fails even at reasonable sample sizes, causing catastrophic collapse in graph recovery accuracy. Method: Through rigorous theoretical analysis and comprehensive simulations, we formally prove that faithfulness violation constitutes an insurmountable performance bottleneck and derive the first theoretical upper bound on structural recovery accuracy for neural causal discovery. Contribution/Results: Experiments demonstrate that state-of-the-art methods substantially deviate from the ground-truth DAG—even on small graphs and large samples. Our work quantifies an inherent ceiling on current paradigm’s performance and calls for a paradigm shift: from assumption-dependent modeling toward robust causal representation learning.

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Latest Papers

Self-Supervised Pretraining of Molecular Graph Encoders with LeJEPA

Sep 02, 2026

研究探讨了自监督预训练对分子图神经网络的影响，采用LeJEPA方法调整分子图编码器，虽然提高了表示学习但未能稳定提升微调效果。

0 citationsRead paper

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish

Feb 27, 2025International Conference on Language Resources and Evaluation

Aspect-Sentiment-Opinion Triplet Extraction (ASTE) lacks annotated resources for Slavic languages, particularly Polish, which has no publicly available dataset. Method: We introduce the first Polish ASTE dataset, covering two domains—hotels and e-commerce—and strictly adhering to the standard English ASTE format to ensure cross-lingual comparability. The dataset is manually annotated with fine-grained sentiment structures and released under a CC-BY-NC license. Contribution/Results: Using this resource, we conduct the first systematic evaluation of two mainstream ASTE paradigms and two Polish large language models, revealing critical performance bottlenecks of existing methods on Slavic languages. This work fills a key gap in low-resource, fine-grained sentiment analysis for Slavic languages and establishes a benchmark dataset and empirical foundation for future multilingual ASTE research and model development.

2 citationsRead paper

Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery

Feb 22, 2025

Neural causal discovery methods face fundamental limitations: they struggle to reliably distinguish true from spurious causal edges under finite samples, and the faithfulness assumption—critical for identifiability—frequently fails even at reasonable sample sizes, causing catastrophic collapse in graph recovery accuracy. Method: Through rigorous theoretical analysis and comprehensive simulations, we formally prove that faithfulness violation constitutes an insurmountable performance bottleneck and derive the first theoretical upper bound on structural recovery accuracy for neural causal discovery. Contribution/Results: Experiments demonstrate that state-of-the-art methods substantially deviate from the ground-truth DAG—even on small graphs and large samples. Our work quantifies an inherent ceiling on current paradigm’s performance and calls for a paradigm shift: from assumption-dependent modeling toward robust causal representation learning.

0 citationsRead paper