A prompting method that forces GPAI models to state SE best practices before deciding reduces prompt-induced cognitive biases by 51% on average across eight tested biases.
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cs.SE 2years
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Traditional ML bias mitigation methods outperform LLM-based methods in both fairness and predictive performance, with prior LLM advantages driven by artificially balanced test data rather than realistic imbalanced distributions.
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Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
A prompting method that forces GPAI models to state SE best practices before deciding reduces prompt-induced cognitive biases by 51% on average across eight tested biases.
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LLMs Are Not a Silver Bullet: A Case Study on Software Fairness
Traditional ML bias mitigation methods outperform LLM-based methods in both fairness and predictive performance, with prior LLM advantages driven by artificially balanced test data rather than realistic imbalanced distributions.