Through our layer-wise context-masking experiments, we identify a clear 'task recognition' point that delineates when large language models shift from in-context learning to performing translation tasks effectively, primarily located at layer 14 out of 32. This finding offers valuable insights into optimizing model performance in machine translation through improved understanding of layer-specific contributions.
The results also indicate that substantial computational savings can be achieved—up to 45%—when utilizing task recognition capabilities effectively. This is particularly evident when utilizing five prompt examples in generating translations. By focusing on layers critical to task recognition, we can fine-tune these models more effectively.
#machine-translation #in-context-learning #large-language-models #task-recognition #computational-efficiency
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