Generative Large Language Model
Generative Large Language Models (LLMs) are powerful AI systems capable of generating human-quality text, enabling advancements in various applications like dialogue systems and machine translation. Current research focuses on improving efficiency (e.g., through quantization and parallel processing), addressing biases and safety concerns (including backdoor attacks), and enhancing performance in low-resource languages via techniques like fine-tuning and prompt engineering. The impact of LLMs is significant, driving progress in natural language processing and impacting diverse fields through improved automation, enhanced accessibility, and more effective information retrieval and analysis.
Papers
SPEED: Speculative Pipelined Execution for Efficient Decoding
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Hasan Genc, Kurt Keutzer, Amir Gholami, Sophia Shao
Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scaling of Texts with Large Language Models
Patrick Y. Wu, Jonathan Nagler, Joshua A. Tucker, Solomon Messing