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
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation
Geonmin Kim, Jaeyeon Kim, Hancheol Park, Wooksu Shin, Tae-Ho Kim
Enhancing Robustness in Language-Driven Robotics: A Modular Approach to Failure Reduction
Émiland Garrabé, Pierre Teixeira, Mahdi Khoramshahi, Stéphane Doncieux
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities
Ryosuke Takata, Atsushi Masumori, Takashi Ikegami
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Xintian Sun, Benji Peng, Charles Zhang, Fei Jin, Qian Niu, Junyu Liu, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang
Grounding Natural Language to SQL Translation with Data-Based Self-Explanations
Yuankai Fan, Tonghui Ren, Can Huang, Zhenying He, X. Sean Wang
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
Ontology Population using LLMs
Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu