Fine-tuning pretrained language models has shown promising results on a wide range of tasks, but when encountering a novel task, do they rely more on generic pretrained representation, or develop brand new task-specific solutions? Here, we fine-tuned GPT-2 on a context-dependent decision-making task, novel to the model but adapted from neuroscience literature. We compared its performance and internal mechanisms to a version of GPT-2 trained from scratch on the same task. Our results show that fine-tuned models depend heavily on pretrained representations, particularly in later layers, while models trained from scratch develop different, more task-specific mechanisms. These findings highlight the advantages and limitations of pretraining for task generalization and underscore the need for further investigation into the mechanisms underpinning task-specific fine-tuning in LLMs.

翻译：微调预训练语言模型在广泛任务上已展现出良好效果，但当遇到全新任务时，模型是更依赖通用的预训练表征，还是发展出全新的任务特定解决方案？本研究在源自神经科学文献的、对模型而言全新的情境依赖性决策任务上对GPT-2进行微调，并将其性能与内部机制与在相同任务上从头训练的GPT-2版本进行比较。结果表明，微调模型高度依赖预训练表征（尤其在深层网络），而从头训练的模型则发展出不同的、更具任务特定性的机制。这些发现揭示了预训练对任务泛化的优势与局限，并强调需要进一步研究大语言模型中任务特定微调的内在机制。