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Related Questions
- How can data augmentation techniques such as paraphrasing, back-translation, and semantic shift be applied to large language models to improve their performance on reading comprehension tasks?
- What are the effects of various text preprocessing techniques, including tokenization, stemming, and lemmatization, on the performance of LLMs on different reading levels?
- Can data augmentation and text preprocessing techniques be used to improve the generalizability of LLMs across different domains and topics?
- How can the use of data augmentation and text preprocessing impact the interpretability of LLMs, particularly in relation to their ability to handle out-of-distribution inputs?
- What are the trade-offs between using more or less data augmentation and text preprocessing in fine-tuning LLMs for different reading levels and comprehension abilities?
- Can the use of transfer learning and multi-task learning be combined with data augmentation and text preprocessing to improve LLMs' performance on reading comprehension tasks?
- How can the effects of data augmentation and text preprocessing be evaluated and measured in the context of fine-tuning LLMs for different reading levels and comprehension abilities?
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