Question then is whether to fine tune an autoregressive LLM or use embeddings and attach a linear head to predict the outcome. Probably the latter.
You could also create viable labels without real life hires. Have a panel of 3 expert judges and give them a pile of 300 CVs and there's your training data. The model is then answering the easier question "would a human have chosen to pursue an interview given this information?" which more closely maps to what you're trying to have the model do anyways.
Then action the model so it only acts as a low confidence first pass filter, removing the bottom 40% of CVs instead of the more impossible task of trying to have it accurately give you the top 10%.
But this is more work than writing a 200 word system prompt and appending the resume and asking ChatGPT, and nobody in HR will be able to notice the difference.
You could also create viable labels without real life hires. Have a panel of 3 expert judges and give them a pile of 300 CVs and there's your training data. The model is then answering the easier question "would a human have chosen to pursue an interview given this information?" which more closely maps to what you're trying to have the model do anyways.
Then action the model so it only acts as a low confidence first pass filter, removing the bottom 40% of CVs instead of the more impossible task of trying to have it accurately give you the top 10%.
But this is more work than writing a 200 word system prompt and appending the resume and asking ChatGPT, and nobody in HR will be able to notice the difference.