Papers by Tongtong Yang
MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)
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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data. |
| Approach: | They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages. |
| Outcome: | Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)
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| Challenge: | Existing approaches to fine-tune large language models are infeasible due to privacy regulations. |
| Approach: | They propose an offsite tuning framework that secures data privacy and model parameter and capability privacy. |
| Outcome: | The proposed framework secures data privacy and model parameter and capability privacy while preserving gradient alignment. |