@inproceedings{chen-etal-2026-llm,
    title = "An {LLM}-Embedding Semantic Adaptation Network for Post-level Semantic Drift Evaluation",
    author = "Chen, Ning  and
      Kang, Mingyu  and
      Li, Jie  and
      L{\textbackslash}{''}u, Linyuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1457/",
    pages = "29165--29176",
    ISBN = "979-8-89176-395-1",
    abstract = "Evaluating semantic drift is essential for understanding dynamical discourse evolution and opinion formation in online discussions. However, sparse and uneven distributions of event-specific keywords prevent traditional models from capturing post-level semantic drift. Thus, to address this issue, we propose an LLM-embedding Semantic Adaptation Network (LLM-SAN), which is a hybrid semantic drift evaluation model with an LLM-Embedding gated recurrent unit (GRU) module, an LLM-Embedding graph convolutional network (GCN) module and a multi-expert adaptive fusion module. The GRU module is used to extract features from event related posts, and The GCN is used to extract features from temporal graphical topic posts. Then, the features are merged by the multi-expert adaptive fusion module. Finally, this module predicts the future post embedding, and the prediction error is used to evaluate and detect the semantic drift points. Extensive experiments are conducted, and the results show that LLM-SAN achieves the state-of-the-art performance on the semantic drift evaluation task, compared to the other baselines. Ablation experiments are also conducted to show the effectiveness of each module in LLM-SAN."
}