I’m currently working on a project where I’m trying to predict JEL codes (Journal of Economic Literature classification codes) based on a list of keywords. I’m using OpenAI’s language models for fine-tuning, specifically Babbage, Carl, and Davinci.
However, I’ve noticed that the results of each model can vary significantly, with surprising results such as Davinci being the worst performer. I’m seeking advice on the design of the prompt and associated completions to optimize the performance of the language model.
I’m considering two possible prompt formats and would like to know which one would be the most suitable for my task.
Prompt format 1:
Keyword 1: Australian dollar Keyword 2: Common currency Keyword 3: Monetary policy Keyword 4: New Zealand
Prompt format 2:
Keywords: DSGE model; Bayesian estimation; Time-varying risk premia; Monetary policy
If anyone has any advice on which prompt format would be more effective for JEL code prediction and why, I would greatly appreciate it. Additionally, if anyone has any resources that could help me better understand and study fine-tuning language models for this type of task, it would be very helpful.
Thank you in advance for your help