Lingxiang Wang — accepted AI-conference papers

2025

Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation

Shiwen Zhang, Lingxiang Wang, Hainan Zhang, Ziwei Wang, Sijia Wen, Zhiming Zheng

EMNLP 2025

In competitive programming task, problem statements are often embedded within elaborate narrative backgrounds, requiring deep understanding of the underlying solutions to successfully complete the tasks. Current code generation models primarily focus on token-level semantic modeling, highly suscepti

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2024

Safely Learning with Private Data: A Federated Learning Framework for Large Language Model

Jia-Ying Zheng, Hainan Zhang, Lingxiang Wang, Wangjie Qiu, Hong-Wei Zheng, Zhi-Ming Zheng

EMNLP 2024main

Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in multiple silos, making its secure utilization for LLM training a challenge. Federated learning (FL) is an ideal solution…

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