0
$\begingroup$

I'm exploring the capabilities of GenAI for text analysis and decision-making processes. I'm particularly interested in understanding whether GenAI can be leveraged to create decision trees directly from textual data.

As an example, let's consider a scenario where we have documents outlining various rules and criteria for approving loans. Are there any GenAI approaches to analyze this document and produce a decision tree that reflects the decision-making process outlined in the text?

To summarize, I'm curious about the feasibility of utilizing GenAI to:

  • Analyze textual data to extract features relevant for decision-making.
  • Automatically generate decision trees based on the identified features.

I'm aware that GenAI is proficient in natural language processing tasks and can generate text based on provided prompts. However, I'm uncertain about its suitability for constructing decision trees from textual data.

Thereby, I'm curious to know if anyone has experience or insights into using GenAI for this purpose. Additionally, any resources, examples, or methodologies on how to implement such a task would be greatly appreciated.

Thank you!

$\endgroup$

1 Answer 1

0
$\begingroup$

The only thing that you can do is fine-tune chatgpt. A whole document would have way too many tokens for any other model to deal with.

$\endgroup$

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .