Machine Unlearning in Large Language Models

• URL: http://arxiv.org/abs/2405.15152v1
• Date: Fri, 24 May 2024 02:12:51 GMT
• Title: Machine Unlearning in Large Language Models
• Authors: Saaketh Koundinya Gundavarapu, Shreya Agarwal, Arushi Arora, Chandana Thimmalapura Jagadeeshaiah,
• Abstract summary: This paper introduces a methodology to align large language models (LLMs) with ethical, privacy, and safety standards. Our approach aims to selectively erase or modify learned information in LLMs, targeting harmful responses and copyrighted content.
• Score: 0.7864304771129751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning, a novel area within artificial intelligence, focuses on addressing the challenge of selectively forgetting or reducing undesirable knowledge or behaviors in machine learning models, particularly in the context of large language models (LLMs). This paper introduces a methodology to align LLMs, such as Open Pre-trained Transformer Language Models, with ethical, privacy, and safety standards by leveraging the gradient ascent algorithm for knowledge unlearning. Our approach aims to selectively erase or modify learned information in LLMs, targeting harmful responses and copyrighted content. This paper presents a dual-pronged approach to enhance the ethical and safe behavior of large language models (LLMs) by addressing the issues of harmful responses and copyrighted content. To mitigate harmful responses, we applied gradient ascent on the PKU dataset, achieving a 75\% reduction in harmful responses for Open Pre-trained Transformer Language Models (OPT1.3b and OPT2.7b) \citet{zhang2022opt} while retaining previous knowledge using the TruthfulQA dataset \citet{DBLP:journals/corr/abs-2109-07958}. For handling copyrighted content, we constructed a custom dataset based on the Lord of the Rings corpus and aligned LLMs (OPT1.3b and OPT2.7b) \citet{zhang2022opt} through LoRA: Low-Rank Adaptation of Large Language Models \citet{DBLP:journals/corr/abs-2106-09685} finetuning. Subsequently, we employed gradient ascent to unlearn the Lord of the Rings content, resulting in a remarkable reduction in the presence of copyrighted material. To maintain a diverse knowledge base, we utilized the Book Corpus dataset. Additionally, we propose a new evaluation technique for assessing the effectiveness of harmful unlearning.

Related papers

• CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept [5.345828824625758]
  We propose a novel amortized unlearning approach using codebook features and Sparse Autoencoders (SAEs) By leveraging a bottleneck to decompose the activation space and regulate information flow, our method efficiently unlearns targeted information while preserving the model's performance on unrelated data.
  arXiv  Detail & Related papers  (2024-10-08T10:26:22Z)
• Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models [25.91643745340183]
  Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. This poses risk of privacy and copyright violations, highlighting the need for efficient machine unlearning methods. We propose two novel techniques for robust and efficient unlearning for LLMs.
  arXiv  Detail & Related papers  (2024-08-13T04:18:32Z)
• Course-Correction: Safety Alignment Using Synthetic Preferences [17.897817682322053]
  We introduce the textscC$2$-Eval benchmark for quantitative assessment and analyze 10 popular language models. Using an automated pipeline, we create textscC$2$-Syn, a synthetic dataset with 750K pairwise preferences. Experiments on 2 LLMs, textscLlama2-Chat 7B and textscQwen2 7B, show that our method effectively enhances course-correction skills without affecting general performance.
  arXiv  Detail & Related papers  (2024-07-23T16:54:28Z)
• MUSE: Machine Unlearning Six-Way Evaluation for Language Models [109.76505405962783]
  Language models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content. We propose MUSE, a comprehensive machine unlearning evaluation benchmark. We benchmark how effectively eight popular unlearning algorithms can unlearn Harry Potter books and news articles.
  arXiv  Detail & Related papers  (2024-07-08T23:47:29Z)
• Avoiding Copyright Infringement via Large Language Model Unlearning [24.050754626661124]
  We propose a novel framework designed to unlearn copyrighted content from Large Language Models over multiple time steps. We improve unlearning efficacy by introducing random labeling loss and ensuring the model retains its general-purpose knowledge. Experimental results show that SSU achieves an effective trade-off between unlearning efficacy and general-purpose language abilities.
  arXiv  Detail & Related papers  (2024-06-16T14:12:37Z)
• Large Language Model Unlearning via Embedding-Corrupted Prompts [10.889859281637406]
  We present textbfEmbedding-COrrupted (ECO) Prompts, a lightweight unlearning framework for large language models. We enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget. We find that these embedding-corrupted prompts not only lead to desirable outputs that satisfy the unlearning objective but also closely approximate the output from a model that has never been trained on the data intended for forgetting.
  arXiv  Detail & Related papers  (2024-06-12T06:56:20Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Generative Context-aware Fine-tuning of Self-supervised Speech Models [54.389711404209415]
  We study the use of generative large language models (LLM) generated context information. We propose an approach to distill the generated information during fine-tuning of self-supervised speech models. We evaluate the proposed approach using the SLUE and Libri-light benchmarks for several downstream tasks: automatic speech recognition, named entity recognition, and sentiment analysis.
  arXiv  Detail & Related papers  (2023-12-15T15:46:02Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• Meta-Learning Online Adaptation of Language Models [88.8947656843812]
  Large language models encode impressively broad world knowledge in their parameters. However, the knowledge in static language models falls out of date, limiting the model's effective "shelf life"
  arXiv  Detail & Related papers  (2023-05-24T11:56:20Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.