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
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction
Haotian Chen, Bingsheng Chen, Xiangdong Zhou
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao