How can you prevent bias amplification in text generation models when using biased data sources

Question

With the help of code can you tell me How can you prevent bias amplification in text generation models when using biased data sources?

Answer 1

 Bias amplification in text generation models can be prevented through techniques like balanced data sampling, debiasing algorithms, controlled text generation, and post-hoc filtering.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses neutral and balanced prompts to avoid introducing bias from the start.
• Implements a simple post-hoc filtering function to flag potentially biased language.
• Controls output length and skips special tokens for clean, focused text generation.

Hence, by combining balanced input, controlled generation, and post-hoc filtering, we minimize the risk of bias amplification in text generation models.