Controlling Conditional Language Models without Catastrophic Forgetting

TL;DR

This paper introduces Conditional DPG (CDPG), a method to adapt pretrained language models for specific control tasks without losing their general capabilities, demonstrated across multiple tasks and models.

Contribution

The paper extends distributional policy gradients to conditional tasks, enabling controlled adaptation of pretrained models without catastrophic forgetting.

Findings

CDPG effectively aligns models with control objectives

It prevents catastrophic forgetting during fine-tuning

Demonstrated on translation, summarization, and code generation

Abstract

Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve a large number of tasks. However, due to their generic training methodology, these models often fail to meet some of the downstream requirements (e.g., hallucinations in abstractive summarization or style violations in code generation). This raises the important question of how to adapt pre-trained generative models to meet all requirements without destroying their general capabilities ("catastrophic forgetting"). Recent work has proposed to solve this problem by representing task-specific requirements through energy-based models (EBMs) and approximating these EBMs using distributional policy gradients (DPG). Despite its effectiveness, this approach is however limited to unconditional distributions. In this paper, we extend DPG to conditional tasks by proposing Conditional DPG (CDPG). We evaluate CDPG on four different control objectives across three tasks (translation, summarization and code generation) and two pretrained models (T5 and GPT-Neo). Our results show that fine-tuning using CDPG robustly moves these pretrained models closer towards meeting control objectives and -- in contrast with baseline approaches -- does not result in catastrophic forgetting.