5 papers
LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
Hao Jiang, Enneng Yang, Guojie Zhu +7
Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedl…
SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking
Weiyang Huang, Xuefeng Bai, Kehai Chen +4
Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive "overthinking", generating unnecessarily long reasoning chains. While curren…
Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
Zhenyu Liu, Dongfang Li, Xinshuo Hu +4
Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader a…
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation
Xinping Zhao, Dongfang Li, Yan Zhong +4
Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG perform…
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion
Xinping Zhao, Jindi Yu, Zhenyu Liu +5
As we all know, hallucinations prevail in Large Language Models (LLMs), where the generated content is coherent but factually incorrect, which inflicts a heavy blow on the widespre…