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Related Questions
- What are the key differences between active learning and human evaluation techniques in addressing bias in language models?
- How can active learning be used to detect bias in language models and what are the benefits and limitations of this approach?
- What human evaluation techniques can be employed to identify and mitigate bias in language models, such as using diverse annotator pools or expert review?
- Can active learning be used in conjunction with human evaluation to reduce bias in language models and how might this be done?
- How can language models be trained to be more transparent about their potential biases and errors, using techniques such as model interpretability or uncertainty estimation?
- What are some common biases that can be detected and reduced using active learning and human evaluation techniques, such as social bias, linguistic bias, or cultural bias?
- Can active learning and human evaluation techniques be used to identify and reduce bias in language models for specific domains, such as healthcare or finance, and if so, how?
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