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cs.CL2025
Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization
Hee-Jin Lee, Zhen Guo, Luchao Jin +1
We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer servic…
cs.CL2025
Synthetic Data RL: Task Definition Is All You Need
Yiduo Guo, Zhen Guo, Chuanwei Huang +5
Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduc…
cs.CV2025
A Token-level Text Image Foundation Model for Document Understanding
Tongkun Guan, Zining Wang, Pei Fu +9
In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). H…