Papers by Weisi Liu
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)
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| Challenge: | Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored . |
| Approach: | They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German. |
| Outcome: | The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German. |
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (2026.acl-long)
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| Challenge: | Existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. |
| Approach: | They develop a temporal adaptive learning framework that captures temporal shifts . they use medical ontology and other knowledge sources to integrate temporal adaptation . |
| Outcome: | The proposed framework improves classification tasks across multiple domains and domains with knowledge integration. |
Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation (2025.findings-emnlp)
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| Challenge: | Genetic Prompt combines genetic algorithms with Large Language Models to augment synthetic data generation. |
| Approach: | They propose a framework that combines genetic algorithms with LLMs to augment synthetic data generation. |
| Outcome: | The proposed framework outperforms state-of-the-art models and shows robust performance across generator models. |