What's your approach to topic research from multiple articles using GPT-3?

In the following scenario, what’s your best approach using GPT-3 API?

  1. You need to come out with a short paragraph, about a specific subject/Topic
  2. You must base your paragraph on a set of articles, 3-6 articles, up to 1000 words each, written in an unknown structure.

Here is what I found so far to work well:

  1. The main constraint is the open ai token limit in the prompt
  2. Due to the constraint, I’d ask OPT-3 to parse unstructured data using the specific subject in the prompt request.
  3. I’ll then iterate each article and save it all into 1 string variable
  4. Then, repeat it one last time but using the new string variable
  5. If the article is too long, I’ll cut it into smaller chunks, based on line breaks or sentences ending with “.”.
  6. Of curse fine-tune, the model with the specific subject before will produce much better results
  7. The temperature should be set to 0, to make sure GPT-3 uses only facts from the data source.

Example: Let’s say I want to write a paragraph about Subject A, Subject B, and Subject C. And I have 5 articles as references. The open ai playground will look something like this:

The open ai playground will look something like this:

Example Article 1

----

Subject A: example A for OPT-3

Subject B: n/a

Subject c: n/a

=========

Example Article 2

----

Subject A: n/a

Subject B: example B for GPT-3

Subject C: n/a

=========

Example Article 3

----

Subject A: n/a

Subject B: n/a

Subject c: example for GPT-3

=========

Article 1
-----

Subject A:

Subject B:

Subject C:

=========

... repeating with all articles, save to str

-----

str

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Subject A:

Subject B:

Subject C: