Our analysis indicates that the number of prompts plays a minimal role in determining the layer at which task recognition occurs in GPTNEO and BLOOM.
Through fine-tuning with lightweight LoRA, we've observed that specific layers can be adapted to better locate the translation task, despite limited supervision.
The findings suggest that while there might be performance variations with context masking in the middle layers, task recognition stabilizes consistently across different number of prompts.
The experiment underscores the adaptability of model layers to recognize tasks like translation, indicating the importance of architectural adjustments in machine translation systems.
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