Papers by Jingzhi Fang

1 papers
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)

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Challenge: Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact.
Approach: They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy.
Outcome: The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency.

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