Soft Prompting for Unlearning in Large Language Models

Karuna Bhaila, Minh-Hao Van, Xintao Wu·June 17, 2024

Summary

These papers investigate soft prompting as a method for unlearning in large language models (LLMs), addressing data protection concerns. The studies introduce Soft Prompting for Unlearning (SPUL), a framework that uses learnable prompt tokens to induce unlearning without updating model parameters. SPUL is evaluated on text classification tasks, demonstrating improved trade-offs between utility and forgetting compared to fine-tuning. It shows effectiveness in sentiment analysis, scalability across LLMs, and the impact of hyperparameters and unlearning data size. The research highlights the potential of soft prompting as a lightweight solution for ethical and privacy-preserving LLM deployment, while also pointing to the need for future work on expanding the framework to other tasks and addressing model security.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of unlearning in large language models (LLMs) to efficiently forget training samples in response to unlearning requests, instead of costly retraining . This problem is not entirely new, as machine unlearning has been explored previously in the context of addressing data protection guidelines and concerns related to bias, toxicity, and privacy in LLMs . The paper specifically focuses on proposing a novel approach called soft prompting for unlearning in LLMs, which is less resource-intensive than traditional fine-tuning methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of the Soft Prompting for Unlearning (SPUL) framework in the context of Large Language Models (LLMs) . The study focuses on evaluating the unlearning efficacy of SPUL on different LLMs, such as OPT-1.3B and LLaMA-2-13B, by assessing its scalability and performance in unlearning tasks . The research seeks to demonstrate the efficiency of SPUL in achieving forget and retain unlearning objectives while preserving model utility, particularly in addressing concerns related to bias, toxicity, and privacy in LLMs .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach to unlearning in Large Language Models (LLMs) through soft prompting . This method is presented as a less resource-intensive alternative to fine-tuning, which is traditionally used for unlearning in LLMs . The concept of soft prompting involves utilizing prompts to guide the model's behavior and responses, aiming to eliminate the influence of unwanted data points on the model's behavior . This approach is particularly relevant in addressing concerns related to bias, toxicity, and privacy in LLMs .

Furthermore, the paper discusses the integration of machine unlearning into the LLM pipeline to mitigate issues arising from sensitive data in pre-training . Machine unlearning is crucial for ensuring that unwanted data points do not impact the model's behavior as if they were never observed during training . Given the challenges posed by the inaccessibility of model and pre-training data, as well as the large size of pre-trained LLMs making re-training impractical, the focus is on fine-tuning approaches to enforce unlearning .

The study also explores the use of prompts to influence the behavior of LLMs, with a specific emphasis on trainable continuous prompts and discrete prompts . By incorporating prompts into the model architecture, researchers have shown improvements in performance on supervised and few-shot tasks . Additionally, the paper highlights the effectiveness of deep prompt tuning in achieving comparable performance to fine-tuning across various tasks and model scales .

Overall, the paper introduces the concept of soft prompting as an innovative method for unlearning in LLMs, emphasizing its potential to address ethical concerns, such as bias, toxicity, and privacy, while offering a more efficient alternative to traditional fine-tuning approaches . The paper introduces the concept of soft prompting as a method for unlearning in Large Language Models (LLMs), offering several characteristics and advantages compared to previous methods.

  1. Characteristics:

    • Soft prompting involves the utilization of trainable prompt parameters to guide the behavior of LLMs, allowing for the removal of unwanted training examples while keeping the pre-trained model parameters frozen .
    • The approach of soft prompting focuses on optimizing a small number of prompt tokens using a multi-objective loss function defined on disjoint training data subsets, representing the forget data to be removed and the retain data to preserve model utility .
    • The study evaluates the unlearning efficacy of the Soft Prompting for Unlearning (SPUL) framework on different LLMs, including OPT-1.3B, LLaMA-2-7B, and LLaMA-2-13B, showcasing scalability and adaptability across various model scales .
  2. Advantages:

    • Compared to traditional methods like fine-tuning, soft prompting offers a more resource-efficient approach to unlearning in LLMs. While retraining from scratch or fine-tuning incurs high computational costs due to the large number of parameters in LLMs, soft prompting significantly reduces the computation cost by optimizing a smaller subset of parameters .
    • Soft prompting demonstrates superior efficiency over fine-tuning-based baselines in terms of execution time. Despite accessing LLM parameters during backpropagation, the execution time required by SPUL for one training epoch is comparable to other methods like GA + KL and GA+GD, making it a more resource-efficient choice .
    • The SPUL framework effectively achieves the forget and retain unlearning objectives, as evidenced by low forget accuracy and F1 scores compared to retain metrics that closely resemble the base model's performance. This indicates the ability of soft prompting to selectively remove unwanted data while maintaining model utility .

In summary, the characteristics of soft prompting, such as trainable prompt parameters and multi-objective loss functions, coupled with its advantages in resource efficiency and efficacy in achieving unlearning objectives, position it as a promising method for addressing unwanted data influence in LLMs compared to traditional approaches like fine-tuning .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of machine unlearning for Large Language Models (LLMs). Noteworthy researchers in this area include Lester et al., Li and Liang, Liu et al., Si et al., and Maini et al. . The key solution mentioned in the paper is Soft Prompting for Unlearning (SPUL), which focuses on a lightweight alternative to achieve unlearning in LLMs through prompt tokens that induce forgetting of specific examples at inference time without updating the LLM parameters. This method aims to enforce forgetting while preserving utility in text classification tasks with LLMs .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the efficacy of the unlearning framework by addressing specific research questions . The experiments aimed to quantify the performance of the SPUL framework in terms of forget and retain sets, model performance, and the number of training parameters and required GPU hours . The study included main results with different large language models, such as LLaMA-2-7B and OPT-1.3B, to assess the unlearning efficacy of SPUL on various LLMs . Additionally, the experiments focused on scalability, efficiency, and the impact of different hyperparameters on the unlearning performance of the SPUL framework .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SST-2 (Stanford Sentiment Treebank) and Yelp polarity datasets for sentiment classification . The study mentions that they perform full fine-tuning of the Large Language Models (LLMs) based on their publicly available implementations . However, the specific mention of the code being open source is not provided in the context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study evaluates the unlearning framework by addressing specific research questions . The results demonstrate the effectiveness of the SPUL framework in promoting unlearning while preserving inference utility . The experiments compare the performance metrics of the original pre-trained language model (LLM) with the fine-tuned base model, showing significant improvements after fine-tuning . Additionally, the study evaluates the unlearning efficacy of SPUL on different LLMs, showcasing its scalability and effectiveness across models with varying parameters . The results indicate that SPUL can achieve the forget and retain unlearning objectives efficiently, with the larger LLMs adapting better to the unlearning task . The experiments also highlight the robustness of SPUL against large forget sets, emphasizing the framework's ability to improve unlearning efficacy with more forget samples .


What are the contributions of this paper?

The contributions of this paper include:

  • Introducing a task of fictitious unlearning for large language models (LLMs) called TOFU .
  • Locating and editing factual associations in GPT .
  • Scalable extraction of training data from (production) language models .
  • Knowledge unlearning for mitigating privacy risks in language models .
  • Unlearning bias in language models by partitioning gradients .
  • Demonstrating the efficacy of the unlearning framework through evaluation based on various metrics .

What work can be continued in depth?

Further research in the field of unlearning in Large Language Models (LLMs) can be expanded in several directions based on the existing literature:

  • Exploring Soft Prompting Efficiency: Future studies could delve deeper into the efficiency of soft prompting for unlearning in various Natural Language Processing (NLP) tasks such as text generation, question answering, and text summarization .
  • Evaluation Pipeline Development: There is a need for the development of a comprehensive evaluation pipeline for LLM unlearning to assess the robustness of the framework against model-stealing attacks, Membership Inference Attacks (MIAs), and jailbreaking attempts .
  • Model Parameter Optimization: Research could focus on optimizing model parameters through gradient ascent and knowledge alignment objectives to maintain model utility while unlearning unwanted responses for specific examples or datasets .
  • Incorporating Machine Unlearning: Further exploration of machine unlearning techniques, such as forgetting training samples efficiently and addressing bias, toxicity, and privacy concerns in LLMs, can be a promising area for continued research .
  • Task-Specific Unlearning Approaches: Investigating task-specific unlearning approaches, like fine-tuning with various knowledge alignment objectives, can help maintain model utility while eliminating unwanted data points from the training process .
  • Second-Order Optimization: Building on frameworks that leverage second-order optimization for influence-based model updates could enhance the effectiveness of unlearning in LLMs .
  • Localization-Based Objectives: Exploring localization-based objectives to identify subsets of model units that need to be unlearned can provide insights into more targeted unlearning strategies .

Tables

4

Introduction
Background
Data Protection Challenges in LLMs
Emergence of Soft Prompting as a Technique
Objective
To explore SPUL as a method for unlearning
Address utility vs. forgetting trade-offs
Promote ethical and privacy-preserving LLM use
Methodology
Data Collection
Task Selection: Text Classification
Dataset Selection and Splitting
Data Preprocessing
Prompt Tokenization and Integration
Data Cleaning and Standardization
Soft Prompting for Unlearning (SPUL) Framework
Learnable Prompt Tokens
Inducing Unlearning without Parameter Updates
Evaluation
Experiments on Sentiment Analysis
Scalability across Different LLM Architectures
Hyperparameter Analysis
Impact of Unlearning Data Size
Results and Findings
Improved Utility-Forgetting Trade-offs
Comparative Analysis with Fine-Tuning
Scalability Demonstrated in Case Studies
Discussion
Strengths and Limitations of SPUL
Potential for Lightweight Deployment
Future Research Directions
Expanding to Other NLP Tasks
Model Security and Robustness
Conclusion
Soft Prompting as a Promising Solution
Implications for Ethical LLM Deployment
Call for Action in the Community
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What method does SPUL employ for unlearning in large language models?
What are the key areas where SPUL demonstrates effectiveness, according to the research?
How does SPUL compare to fine-tuning in terms of utility and forgetting?
What is the primary focus of the papers discussed?

Soft Prompting for Unlearning in Large Language Models

Karuna Bhaila, Minh-Hao Van, Xintao Wu·June 17, 2024

Summary

These papers investigate soft prompting as a method for unlearning in large language models (LLMs), addressing data protection concerns. The studies introduce Soft Prompting for Unlearning (SPUL), a framework that uses learnable prompt tokens to induce unlearning without updating model parameters. SPUL is evaluated on text classification tasks, demonstrating improved trade-offs between utility and forgetting compared to fine-tuning. It shows effectiveness in sentiment analysis, scalability across LLMs, and the impact of hyperparameters and unlearning data size. The research highlights the potential of soft prompting as a lightweight solution for ethical and privacy-preserving LLM deployment, while also pointing to the need for future work on expanding the framework to other tasks and addressing model security.
Mind map
Inducing Unlearning without Parameter Updates
Learnable Prompt Tokens
Model Security and Robustness
Expanding to Other NLP Tasks
Future Research Directions
Potential for Lightweight Deployment
Strengths and Limitations of SPUL
Impact of Unlearning Data Size
Hyperparameter Analysis
Scalability across Different LLM Architectures
Experiments on Sentiment Analysis
Soft Prompting for Unlearning (SPUL) Framework
Dataset Selection and Splitting
Task Selection: Text Classification
Promote ethical and privacy-preserving LLM use
Address utility vs. forgetting trade-offs
To explore SPUL as a method for unlearning
Emergence of Soft Prompting as a Technique
Data Protection Challenges in LLMs
Call for Action in the Community
Implications for Ethical LLM Deployment
Soft Prompting as a Promising Solution
Scalability Demonstrated in Case Studies
Comparative Analysis with Fine-Tuning
Improved Utility-Forgetting Trade-offs
Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results and Findings
Methodology
Introduction
Outline
Introduction
Background
Data Protection Challenges in LLMs
Emergence of Soft Prompting as a Technique
Objective
To explore SPUL as a method for unlearning
Address utility vs. forgetting trade-offs
Promote ethical and privacy-preserving LLM use
Methodology
Data Collection
Task Selection: Text Classification
Dataset Selection and Splitting
Data Preprocessing
Prompt Tokenization and Integration
Data Cleaning and Standardization
Soft Prompting for Unlearning (SPUL) Framework
Learnable Prompt Tokens
Inducing Unlearning without Parameter Updates
Evaluation
Experiments on Sentiment Analysis
Scalability across Different LLM Architectures
Hyperparameter Analysis
Impact of Unlearning Data Size
Results and Findings
Improved Utility-Forgetting Trade-offs
Comparative Analysis with Fine-Tuning
Scalability Demonstrated in Case Studies
Discussion
Strengths and Limitations of SPUL
Potential for Lightweight Deployment
Future Research Directions
Expanding to Other NLP Tasks
Model Security and Robustness
Conclusion
Soft Prompting as a Promising Solution
Implications for Ethical LLM Deployment
Call for Action in the Community
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of unlearning in large language models (LLMs) to efficiently forget training samples in response to unlearning requests, instead of costly retraining . This problem is not entirely new, as machine unlearning has been explored previously in the context of addressing data protection guidelines and concerns related to bias, toxicity, and privacy in LLMs . The paper specifically focuses on proposing a novel approach called soft prompting for unlearning in LLMs, which is less resource-intensive than traditional fine-tuning methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of the Soft Prompting for Unlearning (SPUL) framework in the context of Large Language Models (LLMs) . The study focuses on evaluating the unlearning efficacy of SPUL on different LLMs, such as OPT-1.3B and LLaMA-2-13B, by assessing its scalability and performance in unlearning tasks . The research seeks to demonstrate the efficiency of SPUL in achieving forget and retain unlearning objectives while preserving model utility, particularly in addressing concerns related to bias, toxicity, and privacy in LLMs .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel approach to unlearning in Large Language Models (LLMs) through soft prompting . This method is presented as a less resource-intensive alternative to fine-tuning, which is traditionally used for unlearning in LLMs . The concept of soft prompting involves utilizing prompts to guide the model's behavior and responses, aiming to eliminate the influence of unwanted data points on the model's behavior . This approach is particularly relevant in addressing concerns related to bias, toxicity, and privacy in LLMs .

Furthermore, the paper discusses the integration of machine unlearning into the LLM pipeline to mitigate issues arising from sensitive data in pre-training . Machine unlearning is crucial for ensuring that unwanted data points do not impact the model's behavior as if they were never observed during training . Given the challenges posed by the inaccessibility of model and pre-training data, as well as the large size of pre-trained LLMs making re-training impractical, the focus is on fine-tuning approaches to enforce unlearning .

The study also explores the use of prompts to influence the behavior of LLMs, with a specific emphasis on trainable continuous prompts and discrete prompts . By incorporating prompts into the model architecture, researchers have shown improvements in performance on supervised and few-shot tasks . Additionally, the paper highlights the effectiveness of deep prompt tuning in achieving comparable performance to fine-tuning across various tasks and model scales .

Overall, the paper introduces the concept of soft prompting as an innovative method for unlearning in LLMs, emphasizing its potential to address ethical concerns, such as bias, toxicity, and privacy, while offering a more efficient alternative to traditional fine-tuning approaches . The paper introduces the concept of soft prompting as a method for unlearning in Large Language Models (LLMs), offering several characteristics and advantages compared to previous methods.

  1. Characteristics:

    • Soft prompting involves the utilization of trainable prompt parameters to guide the behavior of LLMs, allowing for the removal of unwanted training examples while keeping the pre-trained model parameters frozen .
    • The approach of soft prompting focuses on optimizing a small number of prompt tokens using a multi-objective loss function defined on disjoint training data subsets, representing the forget data to be removed and the retain data to preserve model utility .
    • The study evaluates the unlearning efficacy of the Soft Prompting for Unlearning (SPUL) framework on different LLMs, including OPT-1.3B, LLaMA-2-7B, and LLaMA-2-13B, showcasing scalability and adaptability across various model scales .
  2. Advantages:

    • Compared to traditional methods like fine-tuning, soft prompting offers a more resource-efficient approach to unlearning in LLMs. While retraining from scratch or fine-tuning incurs high computational costs due to the large number of parameters in LLMs, soft prompting significantly reduces the computation cost by optimizing a smaller subset of parameters .
    • Soft prompting demonstrates superior efficiency over fine-tuning-based baselines in terms of execution time. Despite accessing LLM parameters during backpropagation, the execution time required by SPUL for one training epoch is comparable to other methods like GA + KL and GA+GD, making it a more resource-efficient choice .
    • The SPUL framework effectively achieves the forget and retain unlearning objectives, as evidenced by low forget accuracy and F1 scores compared to retain metrics that closely resemble the base model's performance. This indicates the ability of soft prompting to selectively remove unwanted data while maintaining model utility .

In summary, the characteristics of soft prompting, such as trainable prompt parameters and multi-objective loss functions, coupled with its advantages in resource efficiency and efficacy in achieving unlearning objectives, position it as a promising method for addressing unwanted data influence in LLMs compared to traditional approaches like fine-tuning .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of machine unlearning for Large Language Models (LLMs). Noteworthy researchers in this area include Lester et al., Li and Liang, Liu et al., Si et al., and Maini et al. . The key solution mentioned in the paper is Soft Prompting for Unlearning (SPUL), which focuses on a lightweight alternative to achieve unlearning in LLMs through prompt tokens that induce forgetting of specific examples at inference time without updating the LLM parameters. This method aims to enforce forgetting while preserving utility in text classification tasks with LLMs .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the efficacy of the unlearning framework by addressing specific research questions . The experiments aimed to quantify the performance of the SPUL framework in terms of forget and retain sets, model performance, and the number of training parameters and required GPU hours . The study included main results with different large language models, such as LLaMA-2-7B and OPT-1.3B, to assess the unlearning efficacy of SPUL on various LLMs . Additionally, the experiments focused on scalability, efficiency, and the impact of different hyperparameters on the unlearning performance of the SPUL framework .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SST-2 (Stanford Sentiment Treebank) and Yelp polarity datasets for sentiment classification . The study mentions that they perform full fine-tuning of the Large Language Models (LLMs) based on their publicly available implementations . However, the specific mention of the code being open source is not provided in the context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study evaluates the unlearning framework by addressing specific research questions . The results demonstrate the effectiveness of the SPUL framework in promoting unlearning while preserving inference utility . The experiments compare the performance metrics of the original pre-trained language model (LLM) with the fine-tuned base model, showing significant improvements after fine-tuning . Additionally, the study evaluates the unlearning efficacy of SPUL on different LLMs, showcasing its scalability and effectiveness across models with varying parameters . The results indicate that SPUL can achieve the forget and retain unlearning objectives efficiently, with the larger LLMs adapting better to the unlearning task . The experiments also highlight the robustness of SPUL against large forget sets, emphasizing the framework's ability to improve unlearning efficacy with more forget samples .


What are the contributions of this paper?

The contributions of this paper include:

  • Introducing a task of fictitious unlearning for large language models (LLMs) called TOFU .
  • Locating and editing factual associations in GPT .
  • Scalable extraction of training data from (production) language models .
  • Knowledge unlearning for mitigating privacy risks in language models .
  • Unlearning bias in language models by partitioning gradients .
  • Demonstrating the efficacy of the unlearning framework through evaluation based on various metrics .

What work can be continued in depth?

Further research in the field of unlearning in Large Language Models (LLMs) can be expanded in several directions based on the existing literature:

  • Exploring Soft Prompting Efficiency: Future studies could delve deeper into the efficiency of soft prompting for unlearning in various Natural Language Processing (NLP) tasks such as text generation, question answering, and text summarization .
  • Evaluation Pipeline Development: There is a need for the development of a comprehensive evaluation pipeline for LLM unlearning to assess the robustness of the framework against model-stealing attacks, Membership Inference Attacks (MIAs), and jailbreaking attempts .
  • Model Parameter Optimization: Research could focus on optimizing model parameters through gradient ascent and knowledge alignment objectives to maintain model utility while unlearning unwanted responses for specific examples or datasets .
  • Incorporating Machine Unlearning: Further exploration of machine unlearning techniques, such as forgetting training samples efficiently and addressing bias, toxicity, and privacy concerns in LLMs, can be a promising area for continued research .
  • Task-Specific Unlearning Approaches: Investigating task-specific unlearning approaches, like fine-tuning with various knowledge alignment objectives, can help maintain model utility while eliminating unwanted data points from the training process .
  • Second-Order Optimization: Building on frameworks that leverage second-order optimization for influence-based model updates could enhance the effectiveness of unlearning in LLMs .
  • Localization-Based Objectives: Exploring localization-based objectives to identify subsets of model units that need to be unlearned can provide insights into more targeted unlearning strategies .
Tables
4
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