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How can data pipelines be adapted for LLM applications?
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Data pipelines can be adapted for Language Learning Models (LLMs) applications by integrating preprocessing and post-processing steps specific to textual data. This may include tokenization, normalization, and cleaning of raw input data. Moreover, pipeline modifications should consider the specific requirements of the LLM, such as batch size, learning rate, and evaluation metrics.
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