"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models
This work addresses fairness and transparency issues in natural language processing systems, which is crucial for mitigating adverse effects in downstream applications, though it is incremental in methodology.
The paper tackles the problem of societal biases in large language models (LLMs) by introducing a novel prompt-based approach to uncover hidden stereotypes, enabling the identification of biases encoded within the models' internal knowledge.
Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing tasks. However, these models have been shown to harbor inherent societal biases, or stereotypes, which can adversely affect their performance in their many downstream applications. In this paper, we introduce a novel, purely prompt-based approach to uncover hidden stereotypes within any arbitrary LLM. Our approach dynamically generates a knowledge representation of internal stereotypes, enabling the identification of biases encoded within the LLM's internal knowledge. By illuminating the biases present in LLMs and offering a systematic methodology for their analysis, our work contributes to advancing transparency and promoting fairness in natural language processing systems.