28 citations · 28 across the 7 of their papers we have counts for
8 papers
Robust Reasoning via Dynamic Token Selection for Distribution-Aligned Self-Distillation
Ruiqi Zhang, Lingxiang Wang, Hainan Zhang Zhiming Zheng
Self-distillation improves learning efficiency by rewriting reference answers as training data that better matches the model's own distribution. However, reference answers also int…
LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
Jinwen Chen, Shiwen Zhang, Shuai Gong +6
In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historica…
From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
Ruiqi Zhang, Lingxiang Wang, Hainan Zhang +2
Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly focus on the likelihood-based st…
Parameter Importance-Driven Continual Learning for Foundation Models
Lingxiang Wang, Hainan Zhang, Zhiming Zheng
Domain-specific post-training often causes catastrophic forgetting, making foundation models lose their general reasoning ability and limiting their adaptability to dynamic real-wo…
FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation
Shule Lu, Lingxiang Wang, Sijia Wen +2
With the rapid development of artificial intelligence, dialogue systems have become a prominent form of human-computer interaction. However, traditional centralized or fully local…
Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation
Shiwen Zhang, Lingxiang Wang, Hainan Zhang +3
In competitive programming task, problem statements are often embedded within elaborate narrative backgrounds, requiring deep understanding of the underlying solutions to successfu…
CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain
Lingxiang Wang, Hainan Zhang, Qinnan Zhang +4
Large language models (LLMs) excel at generating code from natural language instructions, yet they often lack an understanding of security vulnerabilities. This limitation makes it…
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
JiaYing Zheng, HaiNan Zhang, LingXiang Wang +3
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 m…