Scaffolding learning: From specific to generic with large language models.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Natural Language Processing

Background:

• Large language models (LLMs) demonstrate proficiency in complex mathematical tasks.
• However, LLMs often fail at fundamental arithmetic operations, indicating potential training deficiencies.
• This highlights a gap in current LLM training methodologies for mathematical reasoning.

Purpose of the Study:

• To propose a novel training approach, Scaffolding Learning, for Large Language Models (LLMs).
• To enhance LLMs' ability to solve mathematical and scientific problems by mimicking human learning progression.
• To investigate the effectiveness of training LLMs on specific foundational skills before general applications.

Main Methods:

• Scaffolding Learning trains LLMs on highly specific operations (e.g., multiplication) first.
• These mastered specific skills are then applied to more generic tasks (e.g., word problems).
• This approach is a specialized form of Curriculum Training, progressing from specific to general tasks.

Main Results:

• Empirical studies demonstrate that LLMs trained with Scaffolding Learning show improved performance.
• Mastery of specific skills requires minimal additional training for application in broader contexts.
• The approach effectively bridges the gap between basic arithmetic and complex problem-solving in LLMs.

Conclusions:

• Scaffolding Learning offers a more effective paradigm for training LLMs in mathematics and science.
• Mimicking step-by-step human learning enhances LLMs' problem-solving capabilities.
• This method shows promise for developing more robust and reliable AI in STEM fields.