Papers by Zitai Qiu
Text is All You Need: LLM-enhanced Incremental Social Event Detection (2025.acl-long)
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| Challenge: | Existing state-of-the-art (SOTA) SED models rely on graph neural networks (GNNs) Existing SED frameworks rely heavily on GNNs, which require complex graph construction and time-consuming training processes. |
| Approach: | They propose a framework that leverages the rich background knowledge of large language models to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions. |
| Outcome: | The proposed framework outperforms existing models on two challenging real-world datasets. |
Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)
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| Challenge: | Social event detection relies on labeled data, but annotation is costly and labor-intensive. |
| Approach: | They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. |
| Outcome: | The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score. |