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Related Questions
- What are the most significant obstacles to overcome when adapting a pre-trained language model for technical translation tasks?
- How can you address the issue of domain-specific terminology and jargon in technical translation using large language models?
- What are some effective strategies for mitigating the risk of cultural and contextual nuances being lost in technical translation?
- What are the key considerations for selecting the optimal dataset size and diversity for fine-tuning a large language model for technical translation?
- How can you evaluate the performance of a fine-tuned language model on technical translation tasks, and what metrics are most relevant?
- What are some common pitfalls to avoid when fine-tuning a large language model for technical translation, such as overfitting or underfitting?
- Can you explain the importance of data preprocessing and normalization in fine-tuning a large language model for technical translation, and how to perform it effectively?
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