most citedKnowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions

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cs.CL2026

Large Language Models for Assisting American College Applications

Zhengliang Liu, Weihang You, Peng Shu +14

American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referen…

cs.CL20261 cited

Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions

Luyang Fang, Xiaowei Yu, Jiazhang Cai +23

The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey p…

cs.CL2025

Evaluation of OpenAI o1: Opportunities and Challenges of AGI

Tianyang Zhong, Zhengliang Liu, Yi Pan +73

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, includi…

cs.CL2025

GP-GPT: Large Language Model for Gene-Phenotype Mapping

Yanjun Lyu, Zihao Wu, Lu Zhang +11

Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits a…

cs.CL2025

AD-GPT: Large Language Models in Alzheimer's Disease

Ziyu Liu, Lintao Tang, Zeliang Sun +11

Large language models (LLMs) have emerged as powerful tools for medical information retrieval, yet their accuracy and depth remain limited in specialized domains such as Alzheimer'…