Papers by Zhizhao Luo

2 papers
Acquisition and Application of Novel Knowledge in Large Language Models (2025.acl-long)

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Challenge: Existing methods for constructing new datasets rely on timestamps or simple templates that do not accurately reflect the real world.
Approach: They propose a knowledge dataset construction approach that simulates biological evolution using knowledge graphs to generate synthetic entities with diverse attributes.
Outcome: The proposed framework outperforms knowledge augmentation methods by 3.3%-38%.
LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)

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Challenge: Experimental results show that topic modeling is competitive compared to closed-source methods.
Approach: They propose a semi-supervised topic modeling method that combines LLMs with clustering to improve topic generation and distribution.
Outcome: The proposed method outperforms state-of-the-art methods that utilize GPT-4 on topic alignment and exhibits competitive performance compared to Neural Topic Models on topic quality.

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