4 citations · 9 across the 9 of their papers we have counts for
6 papers · 1 filter
AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification
Xiaoyu Tan, Tianchu Yao, Chao Qu +8
The reasoning capabilities of advanced large language models (LLMs) like o1 have revolutionized artificial intelligence applications. Nevertheless, evaluating and optimizing comple…
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
Yinghui Li, Haojing Huang, Jiayi Kuang +7
How to alleviate the hallucinations of Large Language Models (LLMs) has always been the fundamental goal pursued by the LLMs research community. Looking through numerous hallucinat…
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Dakuan Lu, Xiaoyu Tan, Rui Xu +5
Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical…
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +5
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decis…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Zhenting Qi, Xiaoyu Tan, Shaojie Shi +3
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks. Nonetheless, this technique often necessitates substantial com…