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
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning, Huan Sun
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim, Hyungjun Kim, Yulhwa Kim, Jae-Joon Kim