Exploring the Effects of Fine-Tuning on Language Models: Insights into Catastrophic Forgetting and Task Inference

核心概念

Fine-tuning language models on specific tasks can lead to catastrophic forgetting of capabilities learned during pretraining, but this forgetting may be more a result of shifted task inference rather than a complete loss of capabilities.

摘要

This paper investigates the effects of fine-tuning language models, particularly the phenomenon of "catastrophic forgetting" where fine-tuning on specific tasks leads to a degradation in performance on other tasks.
The key insights are:

The authors propose a synthetic setup using linear regression tasks to study this phenomenon. They find that fine-tuning on a subset of tasks leads to worse performance on the remaining tasks, but this is not due to a complete "forgetting" of the original capabilities.

The authors hypothesize that fine-tuning skews the model's implicit inference of the task, rather than changing the model's actual capabilities. They propose a "conjugate prompting" strategy that manipulates the prompt to shift the task inference back towards the original pretraining capabilities.

The authors validate this hypothesis on real-world language models, showing that conjugate prompting (e.g. translating prompts to different languages) can recover pretraining capabilities that were suppressed by fine-tuning. This is demonstrated across several settings, including instruction following, natural language reasoning, and safety-critical content generation.

The findings suggest that catastrophic forgetting in language models may be more about a shift in task inference than a complete loss of capabilities. This provides a new perspective on understanding and mitigating the trade-offs introduced by fine-tuning.

統計資料

"We find that the change in loss incurred by fine-tuning is not uniform and depends on the likelihood that the prompt was sampled from the fine-tuning distribution Ddisc."
"For prompts that are likely to be drawn from the fine-tuning distribution, the loss increases as we lower the likelihood. However, this trend does not continue forever and in fact reverses for the continuous prompts."

引述

"We hypothesize that during fine-tuning, the drop in performance on the continuous distribution is largely driven by altered task inference, i.e. for a prompt X, y from Dcont, gθ(X, y) is larger due to the fine-tuning updates."
"Assuming this framework, catastrophic forgetting can be seen as task inference up-weighting fine-tuning tasks and potential degrading pretraining capabilities."

從以下內容提煉的關鍵洞見

Understanding Catastrophic Forgetting in Language Models via Implicit Inference

by Suhas Kotha,... 於 arxiv.org 04-16-2024

https://arxiv.org/pdf/2309.10105.pdf

深入探究

How can we develop fine-tuning methods that better preserve the original pretraining capabilities of language models?

Fine-tuning methods can be improved to better preserve the original pretraining capabilities of language models by incorporating techniques such as Conjugate Prompting. This method involves manipulating the input prompts to shift the model's task inference towards the pretraining distribution, allowing it to recover suppressed capabilities. By designing prompts that are less likely to be drawn from the fine-tuning distribution while still leading to the same solution, we can guide the model to utilize its pretraining knowledge effectively. Additionally, exploring methods that explicitly separate task inference from capabilities, as proposed in the study, can provide a framework for developing fine-tuning algorithms that maintain a balance between adapting to new tasks and retaining existing capabilities.

What are the implications of this task inference perspective on the broader issue of model alignment and safety?

The task inference perspective introduced in the study has significant implications for model alignment and safety. By understanding how fine-tuning affects a model's task inference, we can better address issues of catastrophic forgetting and alignment tax, where fine-tuning for specific tasks leads to a degradation in performance on other tasks. This perspective highlights the importance of maintaining a balance between adapting to new tasks and retaining general capabilities during fine-tuning to ensure model alignment with the intended objectives. Furthermore, by leveraging insights from task inference, we can develop strategies to mitigate safety risks associated with fine-tuning, such as unintended behavior or vulnerabilities introduced through the adaptation process.

Can the insights from this work be extended to other types of machine learning models beyond just language models?

Yes, the insights from this work can be extended to other types of machine learning models beyond just language models. The concept of task inference and the trade-offs between adapting to new tasks and retaining pretraining capabilities are fundamental aspects of model adaptation in various machine learning domains. By applying similar principles of manipulating task inference and designing prompts to guide model behavior, researchers can enhance the fine-tuning process for a wide range of machine learning models. This approach can help improve the robustness, generalization, and safety of models across different applications and domains, providing a more principled framework for model adaptation and deployment.