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
- What are some common sources of data bias in LLMs and how can they be identified?
- How can data curation and preprocessing techniques be used to mitigate data bias?
- What role can data augmentation and oversampling play in reducing bias in LLMs?
- How can model selection and validation methods be used to detect and correct bias?
- What are some strategies for debiasing language models, such as word embeddings and tokenization?
- How can human evaluation and annotation be used to detect and correct bias in LLMs?
- What are some best practices for ensuring fairness and equity in LLMs, such as using diverse training data and avoiding stereotypes?
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