Language Understanding
Language understanding research aims to enable computers to comprehend and process human language as effectively as humans do, focusing on tasks like natural language understanding (NLU) and generation (NLG). Current research emphasizes improving model robustness to noise, ambiguity, and biases, often employing transformer-based architectures, grammar induction techniques, and methods like retrieval-augmented generation and mixture-of-experts to enhance performance on diverse tasks. These advancements have significant implications for various applications, including improved chatbots, more effective machine translation, and enhanced accessibility for individuals with communication challenges.
Papers
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque
Ander Corral, Ixak Sarasua, Xabier Saralegi
Meta-Reflection: A Feedback-Free Reflection Learning Framework
Yaoke Wang, Yun Zhu, Xintong Bao, Wenqiao Zhang, Suyang Dai, Kehan Chen, Wenqiang Li, Gang Huang, Siliang Tang, Yueting Zhuang