ChatGPT-4: Even if the content is "unlearned" from the model, the initial act of using copyrighted content without permission might still be legally actionable

Question 1 of 4 for ChatGPT-4: Please read Who’s Harry Potter? Approximate Unlearning in LLMs https://browse.arxiv.org/pdf/2310.02238.pdf and tell me what the report says.

ChatGPT-4’s answer:Used Ai PDF

The paper titled "Who’s Harry Potter? Approximate Unlearning in LLMs" by Ronen Eldan and Mark Russinovich from Microsoft Research and Microsoft Azure addresses the challenge of unlearning specific subsets of data from Large Language Models (LLMs) without retraining them from scratch. Here's a summary of the report:

Abstract:

• LLMs are trained on vast internet corpora, which often include copyrighted content. This raises legal and ethical challenges.

• The paper introduces a technique to unlearn a subset of training data from an LLM without retraining the entire model.

• The technique was evaluated on unlearning the Harry Potter books from the Llama2-7b model by Meta. The model's ability to generate or recall Harry Potter-related content was effectively erased in about 1 GPU hour of finetuning.

• The technique involves three main components:

  • Using a reinforced model trained on the target data to identify tokens related to the unlearning target.

  • Replacing specific expressions in the target data with generic counterparts and leveraging the model's predictions to generate alternative labels for each token.

  • Finetuning the model on these alternative labels to erase the original text from the model's memory when prompted with its context.

Introduction:

• LLMs encapsulate a vast amount of human knowledge and linguistic patterns. However, they also pose ethical, legal, and technological challenges.

• These models often contain problematic content, including copyrighted texts, toxic data, fake content, and personal data.

• The paper aims to address the challenge of selectively unlearning specific subsets of training data from LLMs without retraining the entire model.

Technique Description:

1. Reinforcement Bootstrapping: The authors propose using a reinforced model that's further trained on the target data. This model has a deeper understanding of the target content. By comparing the reinforced model's predictions with a baseline model, they can identify tokens that need to be unlearned.

2. Anchored Terms: The authors suggest replacing specific terms or entities in the target data with generic counterparts. For instance, replacing "Harry Potter" with a generic name like "Jon" and then using the model's own continuation for the text. The goal is to break the link between specific entities related to the target content and their unique characteristics.

Results:

• The paper provides examples of how the model's responses change after unlearning. For instance, when asked about Harry Potter's best friends, the fine-tuned model responds with generic names like "Sarah and Emily" instead of "Ron Weasley and Hermione Granger."

• The technique effectively reduces the model's ability to recall specific details about the Harry Potter series while maintaining its performance on other benchmarks.

Conclusion:

• The proposed technique offers a way to make LLMs more adaptable and aligned with ethical guidelines, societal values, or specific user requirements.

• While effective in certain cases, the technique might have limitations with other types of content.

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Question 2 of 4 for ChatGPT-4: Is this a feasible method for publishers who want to have their books removed from AI models if the AI makers haven’t asked copyright owners for permission?

ChatGPT-4’s answer:The paper presents a method for "unlearning" specific content from Large Language Models (LLMs) without the need to retrain the entire model from scratch. Based on the summarized content, here are some considerations regarding its feasibility for publishers:

1. Effectiveness: The technique demonstrated that it's possible to effectively erase an LLM's ability to generate or recall specific content, such as the Harry Potter series, in a relatively short amount of time (about 1 GPU hour of finetuning). This suggests that the method can be applied to other copyrighted content as well.

2. Time and Resource Efficiency: One of the main advantages of this method is that it doesn't require retraining the entire model, which can be time-consuming and resource-intensive. Instead, the unlearning process scales with the size of the target content, making it more efficient.

3. Maintained Performance: The paper indicates that after unlearning specific content, the model's performance on common benchmarks remains almost unaffected. This means that the overall utility of the LLM is preserved even after removing specific data.

4. Limitations: The authors do mention potential limitations. While the technique was effective for the Harry Potter series, it might exhibit different results with other types of content, such as non-fiction or textbooks. The method's effectiveness might vary based on the nature and uniqueness of the content.

5. Ethical and Legal Implications: Beyond the technical feasibility, there are ethical and legal considerations. If an LLM has been trained on copyrighted content without permission, the act of unlearning doesn't negate the initial infringement. However, this method offers a way to address copyright concerns post-training.

6. Dynamic Adaptability: The technique can be seen as a step towards more dynamic and adaptable LLMs, allowing models to be fine-tuned post-training to align with ethical guidelines, societal values, or specific user requirements.

In conclusion, while the method presents a promising approach to unlearning specific content from LLMs, its feasibility for publishers would depend on the nature of the content, the effectiveness of the unlearning process, and the legal implications of the initial training. It offers a potential solution for AI developers to address copyright concerns without retraining their models entirely, but further research and real-world applications would be needed to determine its broad applicability.

Question 3 of 4 for AI services: What are the costs involved?ChatGPT-4’s answer:Used Ai PDF

The document does not provide a detailed breakdown of the costs involved in the unlearning process. However, some indirect indicators of potential costs can be inferred:

1. Computational Resources: The paper mentions that the unlearning of the Harry Potter series from the Llama2-7b model took about 1 GPU hour of finetuning. The cost would depend on the type and number of GPUs used, as well as the duration of the finetuning process. For instance, the paper cites that the model was fine-tuned for roughly 30 minutes on 4 A100-GPUs.

2. Human Resources: Manual inspection and evaluation were conducted during the process, which would involve costs related to human labor. For instance, they manually inspected completions during parameter search and compiled a list of prompts with the aid of GPT-4.

3. Infrastructure: The authors thanked someone for helping configure and manage the Azure GPU VMs used for the work, indicating that cloud infrastructure was utilized, which would also have associated costs.

4. Potential Additional Costs: Depending on the content to be unlearned and its complexity, there might be additional costs related to refining the methodology, adapting it to different types of content, and ensuring the model's performance remains consistent.

While these points give an idea of the areas where costs might be incurred, the exact monetary value or detailed breakdown is not provided in the document.

Question 4 of 4 for ChatGPT-4: Do you feel that AI makers should use this approach to remove copyrighted content from their AI models if they have used it in training data sets without permission from rights owners?

ChatGPT-4’s answer:I don't have feelings or opinions, but I can provide an analysis based on the information and ethical considerations surrounding the topic:

1. Ethical Responsibility: Using copyrighted content without permission in training datasets is an infringement of intellectual property rights. AI developers have an ethical responsibility to respect these rights. If an approach like the one described in the paper can effectively remove such content from the model's knowledge, it could be a step towards rectifying the infringement.

2. Legal Implications: Even if the content is "unlearned" from the model, the initial act of using copyrighted content without permission might still be legally actionable. While the unlearning process can mitigate future infringements, it doesn't negate past actions.

3. Effectiveness: The paper demonstrates that the method can effectively remove specific knowledge (like the Harry Potter series) from the model. However, its effectiveness might vary based on the nature and uniqueness of the content. If the method proves consistently effective across various types of content, it could be a viable solution.

4. Maintaining Model Performance: One advantage of the method is that it appears to maintain the model's overall performance on other benchmarks. This means that AI developers can address copyright concerns without significantly compromising the utility of their models.

5. Cost and Resource Considerations: Implementing the unlearning process would require computational resources, time, and potentially manual oversight. AI developers would need to weigh these costs against the benefits of addressing copyright concerns.

6. Preventive Measures: While the unlearning approach can address issues post-training, a more proactive approach would be to ensure that copyrighted content is not included in training datasets without permission in the first place.

In conclusion, while the unlearning approach offers a potential solution to address copyright concerns in trained AI models, its adoption should be based on its effectiveness, ethical considerations, legal implications, and the specific needs and values of AI developers and stakeholders.