Fine-tuned network relies on generic representation to solve unseen cognitive task

TL;DR

This study investigates whether fine-tuned GPT-2 models rely more on their generic pretrained representations or develop new task-specific mechanisms when learning a novel decision-making task, revealing the importance of pretraining in generalization.

Contribution

The paper compares fine-tuned and from-scratch GPT-2 models on a novel task, showing fine-tuning relies heavily on pretrained features, especially in later layers.

Findings

Fine-tuned models depend on pretrained representations in later layers.

Models trained from scratch develop different, task-specific mechanisms.

Pretraining enhances generalization but limits task-specific adaptation.

Abstract

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.