SkyworkAI
Would you provide more information about SkyMath?
Your paper suggested Instruction Boosting and Self-Compare FT would be very helpful but IB looks like Wizard-Evol and IB is very similar to PHP and according to the tech report, I cannot tell what are the differences between them.
The SkyMath method mainly consists of two parts: instruction boosting and self-compare. 1 Instruction boosting primarily draws inspiration from wizardLM and MetaMath. We integrate and improve their methods to enhance instructions, as described in the paper. 2 Self-compare is inspired by PHP, but there are significant differences between them. PHP mainly uses progressive hints in reasoning process, while self-compare emphasizes that the LLM compares previous answers with standard solutions during training.
Thanks for explaining but it's just a paraphrase version of the section in your paper. MetaMath opensourced their example data and used prompt so people can easily verify and reproduce their work. WizardLM also gave their code and final dataset. And your work surppassed both, so I was wondering if more details can be shared to help others find out what really matters in making up such datasets.