Papers by Junyao Yang

4 papers
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration (2025.acl-long)

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Challenge: Existing privacy protection methods for large language models suffer from performance degradation or large inference time overhead.
Approach: They propose a plug-and-play method to protect the privacy of user inputs during LLM inference . they use offline restoration vectors to train restoration vector for each privacy span type .
Outcome: The proposed method can prevent the linear growth of the privacy budget.
ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging (2026.acl-long)

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Challenge: Existing models with long chain-of-thought reasoning lack reasoning depth and domain-specific utility.
Approach: They propose a model merging framework that integrates reasoning with domain-specific task models.
Outcome: The proposed model merging framework outperforms state-of-the-art models while maintaining robust reasoning performance.
RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis (2025.emnlp-main)

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Challenge: Existing solutions to fine-tune large language models for domain-specific tasks are ineffective in addressing privacy concerns.
Approach: They propose a privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.
Outcome: The proposed framework fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation.
DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

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Challenge: Dongba pictographic is the only pictograph script still in use in the world.
Approach: DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome: The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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