Fairness of ChatGPT and the Role Of Explainable-Guided Prompts
Yashar Deldjoo
Our research investigates the potential of Large-scale Language Models
(LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary
classification task. Our findings suggest that LLMs, when directed by
judiciously designed prompts and supplemented with domain-specific knowledge,
can parallel the performance of traditional Machine Learning (ML) models.
Intriguingly, they achieve this with significantly less data-40 times less,
utilizing merely 20 data points compared to the ML's 800. LLMs particularly
excel in minimizing false positives and enhancing fairness, both being vital
aspects of risk analysis. While our results did not surpass those of classical
ML models, they underscore the potential of LLMs in analogous tasks, laying a
groundwork for future explorations into harnessing the capabilities of LLMs in
diverse ML tasks.