Inference-time Unlearning Using Conformal Prediction

TL;DR

This paper introduces an inference-time unlearning framework for generative models that uses conformal prediction and a verifier to efficiently remove specific information without retraining, achieving significant error reduction.

Contribution

It proposes a novel inference-time unlearning method leveraging conformal prediction and a verifier, avoiding parameter updates and improving efficiency and guarantees.

Findings

Reduces unlearning error by up to 93%

Outperforms existing methods on challenging benchmarks

Provides distribution-free unlearning guarantees

Abstract

Machine unlearning is the process of efficiently removing specific information from a trained machine learning model without retraining from scratch. Existing unlearning methods, which often provide provable guarantees, typically involve retraining a subset of model parameters based on a forget set. While these approaches show promise in certain scenarios, their underlying assumptions are often challenged in real-world applications -- particularly when applied to generative models. Furthermore, updating parameters using these unlearning procedures often degrades the general-purpose capabilities the model acquired during pre-training. Motivated by these shortcomings, this paper considers the paradigm of inference time unlearning -- wherein, the generative model is equipped with an (approximately correct) verifier that judges whether the model's response satisfies appropriate unlearning guarantees. This paper introduces a framework that iteratively refines the quality of the generated responses using feedback from the verifier without updating the model parameters. The proposed framework leverages conformal prediction to reduce computational overhead and provide distribution-free unlearning guarantees. This paper's approach significantly outperforms existing state-of-the-art methods, reducing unlearning error by up to 93% across challenging unlearning benchmarks.