WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models

TL;DR

This paper introduces WPN, a novel unlearning method based on N-pair contrastive learning, which effectively reduces harmful outputs in language models while preserving overall performance.

Contribution

The paper proposes WPN, a new unlearning approach that mitigates harmful knowledge in language models without significantly degrading their performance.

Findings

WPN reduces harmful responses to up to 95.8%.

Maintains less than 2% performance degradation on benchmarks.

Demonstrates robustness against out-of-distribution and adversarial attacks.

Abstract

Generative language models (LMs) offer numerous advantages but may produce inappropriate or harmful outputs due to the harmful knowledge acquired during pre-training. This knowledge often manifests as undesirable correspondences, such as "harmful prompts" leading to "harmful outputs," which our research aims to mitigate through unlearning techniques.However, existing unlearning methods based on gradient ascent can significantly impair the performance of LMs. To address this issue, we propose a novel approach called Weighted Positional N-pair (WPN) Learning, which leverages position-weighted mean pooling within an n-pair contrastive learning framework. WPN is designed to modify the output distribution of LMs by eliminating specific harmful outputs (e.g., replacing toxic responses with neutral ones), thereby transforming the model's behavior from "harmful prompt-harmful output" to "harmful prompt-harmless response".Experiments on OPT and GPT-NEO LMs show that WPN effectively reduces the proportion of harmful responses, achieving a harmless rate of up to 95.8\% while maintaining stable performance on nine common benchmarks (with less than 2\% degradation on average). Moreover, we provide empirical evidence to demonstrate WPN's ability to weaken the harmful correspondences in terms of generalizability and robustness, as evaluated on out-of-distribution test sets and under adversarial attacks.