What you'll learn about:

• Mathematical reasoning for LLMs: Key use cases and areas where math reasoning fundamentally enhances language model capabilities.

• Auto-formalization in math: AlphaProof, Lean, and the applicability of automated verifiers for LLM training and evaluation.
  
  

• Expert-curated data collection: Best practices for sourcing high-quality datasets to assess and enhance model performance, including for university-level mathematical reasoning.
  
  

• Designing effective benchmarks: Developing robust evaluation metrics, assessing model performance across different domains, and extracting actionable insights. 
  
  

• Boosting LLM performance with new data: Optimally leveraging a custom university-level training dataset to enhance the mathematical proficiency of top-performing LLMs.
  
  

• Practical applications: Applying the results in academic, educational, and product contexts.