You are to prepare an instruction-tuning dataset for your own LLaMa model. (PS: Do not fine-tune a model create a dataset). You need to take raw information from investopedia.com and perform 2 transformations on it:

• Chain of Thought Generation:

Given an article like **this** on analyzing a mutual fund’s performance, can it convert it into a set of question-answer pairs as shown below:

1. What is the process for analyzing mutual funds?
Mutual funds are usually analyzed by weighting stocks by sector and figuring out how much management contributed to the portfolio.

2. How do you evaluate the performance of a mutual fund?
There are thousands of mutual funds in the U.S. Vanguard offers 266 funds, and its top 50 performers (by 10-year average returns) had an annual average return of 12.4%.

.... and so on

Once you have generated the questions, you can use GPT-3.5/GPT-4 to test the overall article coverage of your generated questions.

• Similar Question Generation:

To broaden the dataset with semantically similar questions you could also generate up to 6 similar questions using an LLM either in a 0-shot manner or in the context of the answer. Before adding these new questions to the original dataset, your goal should be to compare the quality improvement in contextually generated similar questions vs. 0-shot results. So the format would be with auto-grading from GPT-4:

(question, answer, [contextual similar questions],[0 shot similar questions])

To understand more about this task you can look up unnatural LLMs and their usefulness as Meta has mentioned here.