Natural Language
Natural language processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. Current research heavily utilizes large language models (LLMs), such as BERT and others, to tackle diverse tasks including text-to-SQL translation, semantic analysis of images, and even controlling robots via natural language commands. The field's impact spans various sectors, from improving search engines and e-commerce platforms to advancing healthcare diagnostics and facilitating more efficient scientific research through automated literature analysis and data extraction.
Papers
Generation-Augmented Query Expansion For Code Retrieval
Dong Li, Yelong Shen, Ruoming Jin, Yi Mao, Kuan Wang, Weizhu Chen
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
Mehran Kazemi, Najoung Kim, Deepti Bhatia, Xin Xu, Deepak Ramachandran
Controllable Text Generation with Language Constraints
Howard Chen, Huihan Li, Danqi Chen, Karthik Narasimhan
A Natural Bias for Language Generation Models
Clara Meister, Wojciech Stokowiec, Tiago Pimentel, Lei Yu, Laura Rimell, Adhiguna Kuncoro
Natural Language to Code Generation in Interactive Data Science Notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton