Larger Language Model
Large language models (LLMs) are massive neural networks trained to predict the next word in a sequence, acquiring vast knowledge from massive text corpora. Current research focuses on improving their efficiency and performance, particularly in specialized domains, through techniques like fine-tuning smaller models, data augmentation, and retrieval-augmented generation. These advancements are impacting various fields, including healthcare, finance, and software development, by enabling more efficient and accurate natural language processing tasks, though challenges remain in areas like subjective reasoning and mitigating biases.
Papers
Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
Rishabh Bhardwaj, Soujanya Poria
Scope is all you need: Transforming LLMs for HPC Code
Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren