In the following scenario, what’s your best approach using GPT-3 API?
- You need to come out with a short paragraph, about a specific subject/Topic
- 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:
- The main constraint is the open ai token limit in the prompt
- Due to the constraint, I’d ask OPT-3 to parse unstructured data using the specific subject in the prompt request.
- I’ll then iterate each article and save it all into 1 string variable
- Then, repeat it one last time but using the new string variable
- If the article is too long, I’ll cut it into smaller chunks, based on line breaks or sentences ending with “.”.
- Of curse fine-tune, the model with the specific subject before will produce much better results
- The
temperature
should be set to0
, 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
-----
Subject A:
Subject B:
Subject C: