Fine-tuned network relies on generic representation to solve unseen cognitive task
<|reference_start|>Fine-tuned network relies on generic representation to solve unseen cognitive task: 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.<|reference_end|>
@article{lin2024fine-tuned,
    title={Fine-tuned network relies on generic representation to solve unseen
  cognitive task},
    author={Dongyan Lin},
    journal={arXiv preprint arXiv:2406.18926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18926},
    primaryClass={cs.LG}
}