Welcome to the FAQ page for Infermatic.ai! Here, you can find answers to your questions about large language models and the AI industry. Whether you’re curious about how to use our tools or want to learn more about AI, this page is a great place to start.
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Related Questions
- Can data augmentation techniques, such as paraphrasing or back-translation, inadvertently amplify existing biases in the training data?
- How do data selection practices, like filtering or sampling, impact the representation of underrepresented groups in summarization models?
- Can the use of biased data sources, such as news articles with a particular political slant, introduce biases in summarization models?
- Do data augmentation techniques, like adversarial training, help mitigate biases in summarization models?
- Can the selection of training data, such as choosing articles from a specific domain, introduce biases in summarization models?
- How do the evaluation metrics used to assess summarization models impact the introduction of biases?
- Can the use of pre-trained language models, which may have been trained on biased data, introduce biases in summarization models?
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