Papers by Shuo Yin
ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Deep Learning Tensor Programs (2023.emnlp-main)
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| Challenge: | Compilation-based methods with performance models have poor measurement accuracy and transferability between platforms. |
| Approach: | They propose a compiler that automatically generates tensors and automatically tunes them for different hardware platforms. |
| Outcome: | The proposed model reduces inference time and costs on modern DNN benchmarks. |
MuMath: Multi-perspective Data Augmentation for Mathematical Reasoning in Large Language Models (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) that integrate with external Python interpreters are not able to demonstrate the calculation process, which compromises user-friendliness and understanding of problem-solving steps. |
| Approach: | They propose to use LLaMA-2 to refine LLti-perspective augmentation methods to improve performance. |
| Outcome: | The proposed model achieves 88.3% on GSM8K and 34.5% on MATH. |
VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)
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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu
| Challenge: | Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. |
| Approach: | They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. |
| Outcome: | The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency. |
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning (2024.emnlp-main)
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| Challenge: | a method to combine the advantages of open-source and tool-free LLMs remains to be explored. |
| Approach: | They propose a method to integrate open-source LLMs with external Python interpreters and augment math reasoning data. |
| Outcome: | The proposed method improves on GSM8K and MATH with the use of external tools. |
ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)
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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin
| Challenge: | Mainstream research in natural language processing has focused on high-resource and modern languages. |
| Approach: | They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model . |
| Outcome: | The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer. |