Abstract: MAmmoTH2: Scaling Instructions from the Web by Yue et al. provides a new pathway for enhancing LLM reasoning capabilities using an innovative data harvesting method.

• Recalls relevant documents to extract instruction-response pairs from the web.
• Fine-tunes base LLMs with this harvested dataset to build MAmmoTH2 models.
• Demonstrates significant performance improvements on reasoning and chatbot benchmarks.
• Offers an alternative to costly human annotation or reliance on GPT-4 distillation for instruction tuning.

Opinion: The methodology presents a cost-effective and scalable alternative for improving the reasoning abilities of LLMs. By leveraging naturally existing instruction data, this research could revolutionize the way we tune and utilize these models in practical applications.