Reducing Bias in Language Generation: A Research-Oriented Approach

In this article, we propose a novel approach to debias language models by leveraging adversarial prompt engineering techniques. We present a two-stage method that first extracts words or short sentences from multiple toxicity and bias sources, and then expands the list of terms using entity linking models and Sentence-BERT. Our approach acknowledges the complexities of bias and recognizes the importance of demographic sensitivity in language generation.
We analyze a range of training corpora, including web crawl data, news articles, encyclopedias, and HackerNews, to identify frequencies of demographic terms. We excluded papers and multilingual data to focus on English-language sources. Our findings reveal a significant imbalance in the representation of gender and racial groups in these corpora, highlighting the need for debiasing techniques that address these disparities.
Our proposed method utilizes a generative adversarial network (GAN) architecture to generate adversarial prompts that can detect and mitigate bias in language models. We evaluate our approach using a test set of WikiText-103 passages, and demonstrate improved performance metrics compared to baseline models. Our results show that incorporating adversarial prompts into the generation process can significantly reduce per-token latency and memory usage, indicating better performance and efficiency in debiasing language models.
By leveraging adversarial prompt engineering techniques, we aim to provide a more equitable and inclusive approach to language generation that promotes diversity and avoids harmful biases. Our method has broad applications in natural language processing, machine learning, and artificial intelligence, and can be adapted for use in various domains and contexts.
In summary, our article presents a novel debiasing technique for language models based on adversarial prompt engineering. By recognizing the complexities of bias and leveraging GAN architecture, we demonstrate improved performance metrics and a more inclusive approach to language generation. Our findings have significant implications for the development of more equitable and efficient natural language processing systems.