Papers by Zeliang Tong
EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data. |
| Approach: | They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. |
| Outcome: | The proposed framework shows consistent performance improvements on four benchmarks. |
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |