Dynamic Detection of Sarcasm Topic-Target Pairs via LLM-Based Knowledge Alignment
Mengyu Xiang, Yuxuan Song, Qiudan Li, Shu Lei Wu, Daniel Dajun Zeng · 2025
Dynamically capturing topic-target pairs can provide a mechanism explaining the reason that triggers the sarcasm. Existing approaches ignore the event evolution in real-world scenario. Hot topics reflecting the trends of events provide external knowledge clues such as topic tags and posts that change over time. How to eliminate the noise and conflicts existed in the knowledge at different time is a key challenge. This paper proposes a Knowledge Alignment method based on a Large Language Model (KA-LLM) for dynamic detection of topic-target pairs. Guided by knowledge clues, the LLM dynamically adjusts the topological structure of the knowledge graph, enabling the knowledge features to pay more attention to the current events. A hybrid alignment approach is designed to achieve knowledge fusion through feature contrast and reconstruction. The effectiveness of the proposed method is validated on digital and automobile datasets.