Papers by Ruicheng Yin
SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness often depends on costly commercial APIs or cloud services. |
| Approach: | They propose a dual-mode compatible approach that fine-tunes models through shortest-response preference optimization and a confidence-aware rejection mechanism. |
| Outcome: | The proposed approach reduces redundant outputs and response times while reducing computational costs by over 50% and cascade latency by over 80%. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Improving Continual Pre-training Through Seamless Data Packing (2025.findings-acl)
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| Challenge: | Empirical evaluations across various model architectures and corpus domains demonstrate the effectiveness of our method, outperforming baselines in 99% of all settings. |
| Approach: | They propose a method that uses a sliding window technique to pack data before continual pre-training to preserve contextual information and enhance model performance. |
| Outcome: | Empirical evaluations across various model architectures and corpus domains demonstrate the effectiveness of the proposed method outperforming baselines in 99% of settings. |