Showing 2024Show all
2 papers · 1 filter
cs.CL2024
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors
Alex Chandler, Devesh Surve, Hui Su
Accurate text summarization is one of the most common and important tasks performed by Large Language Models, where the costs of human review for an entire document may be high, bu…
cs.LG2024
Unraveling the Mystery of Scaling Laws: Part I
Hui Su, Zhi Tian, Xiaoyu Shen +1
Scaling law principles indicate a power-law correlation between loss and variables such as model size, dataset size, and computational resources utilized during training. These pri…