11 papers
Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding
Ziheng Li, Zexu Sun, Jinman Zhao +8
Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, existing RLV…
Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks
Linbo Cao, Jinman Zhao
As frontier language models increasingly saturate standard QA benchmarks, concerns about data contamination, memorization, and escalating dataset creation costs persist. We propose…
MMREC: LLM Based Multi-Modal Recommender System
Jiahao Tian, Jinman Zhao, Zhenkai Wang +1
The importance of recommender systems is growing rapidly due to the exponential increase in the volume of content generated daily. This surge in content presents unique challenges…
PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy
Shuhao Guan, Moule Lin, Cheng Xu +5
This paper introduces PreP-OCR, a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual con…
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter-Efficient Fine-Tuning of Large Models
Xueyan Zhang, Jinman Zhao, Zhifei Yang +4
This paper introduces Uniform Orthogonal Reinitialization Adaptation (UORA), a novel parameter-efficient fine-tuning (PEFT) approach for Large Language Models (LLMs). UORA achieves…
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas
Jinman Zhao, Zifan Qian, Linbo Cao +5
Role-play in large language models (LLMs) enhances their ability to generate contextually relevant and high-quality responses by simulating diverse cognitive perspectives. However,…