Large Scale Language Model
Large-scale language models (LLMs) are powerful AI systems designed to understand and generate human-like text, aiming to improve various natural language processing tasks. Current research focuses on enhancing LLM efficiency through techniques like iterative refinement, hierarchical architectures, and model compression methods such as quantization and pruning, as well as improving their reliability and addressing issues like hallucinations. These advancements are driving significant progress in diverse fields, including recommendation systems, mental health support, and legal document drafting, demonstrating LLMs' practical impact and their potential to revolutionize numerous applications.
Papers
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (Published in Findings of EMNLP 2024)
Yubo Wang, Xueguang Ma, Wenhu Chen
Automatic Diary Generation System including Information on Joint Experiences between Humans and Robots
Aiko Ichikura, Kento Kawaharazuka, Yoshiki Obinata, Koki Shinjo, Kei Okada, Masayuki Inaba
Large-scale Language Model Rescoring on Long-form Data
Tongzhou Chen, Cyril Allauzen, Yinghui Huang, Daniel Park, David Rybach, W. Ronny Huang, Rodrigo Cabrera, Kartik Audhkhasi, Bhuvana Ramabhadran, Pedro J. Moreno, Michael Riley
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer
Dongqi Pu, Vera Demberg