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
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
Wasu Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, Kevin Ellis
Neural Models for Source Code Synthesis and Completion
Mitodru Niyogi
Text2Data: Low-Resource Data Generation with Textual Control
Shiyu Wang, Yihao Feng, Tian Lan, Ning Yu, Yu Bai, Ran Xu, Huan Wang, Caiming Xiong, Silvio Savarese
Arrows of Time for Large Language Models
Vassilis Papadopoulos, Jérémie Wenger, Clément Hongler
Rethinking Interpretability in the Era of Large Language Models
Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu