Studying synthetic data’s impact on LLMs’ math reasoning. #AIResearch

The content discusses the challenges faced by large language models (LLMs) due to the scarcity of high-quality internet data and the shift towards using synthetic data for training. It explores the impact of positive and negative synthetic data on LLM math reasoning capabilities, highlighting the need for careful construction and utilization of both types of data. The study presents a detailed architecture for generating and utilizing synthetic data, including a synthetic data pipeline, dataset construction, and learning algorithms. It reveals that positive data scaling improves performance but at a slower rate than pre-training, while self-generated positive data outperforms data from larger models. Incorporating negative data and employing reinforcement learning techniques can significantly enhance LLMs’ mathematical reasoning abilities. The study emphasizes the importance of balancing positive and negative synthetic data to optimize LLM performance in math reasoning tasks. Researchers from Carnegie Mellon University, Google DeepMind, and MultiOn conducted the study, offering valuable insights into the use of synthetic data for enhancing LLM capabilities. The study’s findings provide a roadmap for optimizing synthetic data use in LLM training for mathematical reasoning tasks.

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Source link: https://www.marktechpost.com/2024/06/30/this-ai-paper-from-cmu-and-google-deepmind-studies-the-role-of-synthetic-data-for-improving-math-reasoning-capabilities-of-llms/?amp