AS-ES learning introduces a new training paradigm for small models to improve CoT learning efficiency. The method involves segmenting CoT data into Extractive Segments (ES) and Abstractive Segments (AS). This approach enhances logical reasoning capabilities without altering the model or requiring extra data. Experimental results show improved performance on tasks like Math Word Problems and PET summarization. The study explores the impact of segmentation strategies, model sizes, and hyperparameters on the effectiveness of AS-ES learning.
In un'altra lingua
dal contenuto originale
arxiv.org
Approfondimenti chiave tratti da
by Nuwa Xi,Yuha... alle arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01969.pdfDomande più approfondite