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Related Questions
- What are the most common biases that can be present in the query selection process for NLP, and how can they be mitigated?
- How can the distribution of query examples in active learning be controlled to promote diversity, and what methods can be used to monitor and adjust the process?
- What is the impact of sampling bias on the generalizability of NLP models, and how can query selection strategies be designed to address this issue?
- How can the cost of querying a large dataset be optimized to ensure diversity in query selection, particularly in scenarios with limited budget or resources?
- What role does transfer learning play in promoting diversity in query selection, and how can it be used to adapt NLP models to new domains or tasks?
- What are some best practices for evaluating the diversity of query examples in active learning, and how can this evaluation be used to inform the query selection process?
- How can uncertainty-based sampling be used to promote diversity in query selection, and what are some common challenges associated with implementing this approach?
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