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
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian, Yonghui Wu
Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference
Baolin Li, Yankai Jiang, Vijay Gadepally, Devesh Tiwari
Reasoning over Uncertain Text by Generative Large Language Models
Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
HiRE: High Recall Approximate Top-$k$ Estimation for Efficient LLM Inference
Yashas Samaga B L, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli