WizardLM: Evol-Instruct Method | Chess Analogy ... AlphaZero Moment for AI
WizardLM: Trained using highly Specialized Evol-Instruct Method can follow instructions better than ChatGPT
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TrimTask Blog : https://trimtask.com/index.php/2023/0...
WizardLM Research Paper : https://arxiv.org/pdf/2304.12244.pdf
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https://writings.stephenwolfram.com/2...
To improve the performance of large language models, the instruction data for training data needs to be of various difficulty levels. ChatGPT rely upon human annotators to produce this instruction set. The whole annotating process is extremely expensive and time-consuming. On the other hand, the difficulty level distribution of human-created instructions is skewed towards being easy or moderate, with fewer difficult ones. Well, you could spend hours and hours creating instruction data with different levels of difficulty. However, the larger the model, the more difficult and time-consuming it is to train.
Why, you ask? Well, the whole annotating process is no joke. It’s like trying to find a needle in a haystack, except the needle is a difficult instruction and the haystack is a bunch of easy and moderate ones. Plus, finding human annotators who are experts in creating complex instructions is tough. And let’s not forget that humans get tired, too. They can’t keep up a high intensity of work for too long, which means they may not produce enough high-difficulty instructions.
So, what’s the solution?
Developing an automatic method that can mass-produce open-domain instructions at a low cost is the key to advancing language models.
There has to be a better way!
So researchers working on this thought exactly like this and they went and developed an automatic method to help improve our language models.
WizardLM is a breakthrough in language modeling that shows that smaller models can achieve remarkable results on complex tasks. This could have implications for reducing the cost and time of training large models, as well as improving the quality and diversity of instruction-following applications. WizardLM is also an example of how large language models can be used to generate data for other models, creating a feedback loop of improvement.
In conclusion, WizardLM has proven that even smaller models can be highly effective on specific tasks. While larger models like ChatGPT may perform better overall, WizardLM has shown that it can beat ChatGPT on certain tasks. As language models continue to evolve, we may see more task-specific models that can perform even better than the general ones.