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Related Questions
- Can multi-task learning be used to improve the accuracy of sentiment analysis and named entity recognition tasks?
- How can multi-task learning be applied to improve the contextual understanding of language models in tasks such as question answering and text classification?
- What are some real-world applications of multi-task learning in natural language processing, such as improving machine translation and text summarization?
- Can multi-task learning be used to improve the robustness of language models to out-of-vocabulary words and domain shifts?
- How does multi-task learning affect the interpretability of language models, and are there any techniques to improve interpretability while maintaining performance?
- Can multi-task learning be used to improve the performance of language models on tasks that require common sense and world knowledge, such as story completion and conversational dialogue?
- What are some challenges and limitations of applying multi-task learning to large-scale language models, and how can they be addressed?
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