Natural Language Processing Task
Natural Language Processing (NLP) research currently focuses heavily on leveraging Large Language Models (LLMs) to improve the accuracy and efficiency of various tasks. Key areas of investigation include mitigating LLMs' susceptibility to hallucinations (generating inaccurate information), optimizing their deployment across different hardware platforms (including edge devices), and developing robust evaluation methods that go beyond simple metrics. These advancements are significant because they address critical limitations of LLMs, paving the way for more reliable and accessible NLP applications in diverse fields like healthcare, fraud detection, and machine translation.
Papers
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models
Ziyu Zhuang, Qiguang Chen, Longxuan Ma, Mingda Li, Yi Han, Yushan Qian, Haopeng Bai, Zixian Feng, Weinan Zhang, Ting Liu
A Survey on Model Compression for Large Language Models
Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang