Enabling Personalized On-Device Large Language Model Adaptation through Self-Supervised Data Selection and Synthesis

Główne pojęcia

A novel framework to enable on-device personalization of large language models by selecting and storing the most representative data in a self-supervised manner, and generating additional semantically similar data to enhance fine-tuning quality, while minimizing the need for frequent user annotations.

Streszczenie

The proposed framework addresses the challenges of on-device large language model (LLM) personalization, where user-generated data is usually sensitive and private, and uploading such data to the cloud for annotation is not preferred. The framework consists of three key components:

Data Selection: The framework selects and stores the most representative data in a small on-device buffer based on three quality metrics - entropy of embedding (EOE), domain-specific score (DSS), and in-domain dissimilarity (IDD). This self-supervised data selection process does not require any user annotations.

Data Synthesis: To enhance the fine-tuning quality with the limited data in the buffer, the framework uses the LLM to generate additional semantically similar dialogue sets for each selected data point. This provides more labeled data for fine-tuning without additional user input.

Fine-tuning: The selected and synthesized data are used to fine-tune the pre-trained LLM on the device using parameter-efficient techniques like LoRA.

Experimental results on diverse datasets show that the proposed framework outperforms vanilla baselines by up to 38% in ROUGE-1 score, while greatly improving the fine-tuning speed. This is the first work on enabling efficient on-device personalization of large language models.

Statystyki

The average entropy of embedding (EOE) for the selected dialogue sets is higher than the baselines, indicating they contain more informative features.
The domain-specific score (DSS) of the selected dialogue sets is higher than the baselines, showing they are more relevant to the target domains.
The in-domain dissimilarity (IDD) of the selected dialogue sets is higher than the baselines, meaning they bring more new information to the dominant domain.
Increasing the buffer size from 704KB to 11264KB leads to a 38% improvement in ROUGE-1 score on the MedDialog dataset.

Cytaty

"To the best of our knowledge, this is the very first on-device LLM personalization framework."
"Experimental results on multiple datasets of varying temporal correlation including ALPACA, DOLLY, MedDialog, Prosocial-Dialog, OPENORCA, and Empathetic-Dialog show that the proposed framework achieves up to 38% higher ROUGE-1 than the baselines and at the same time greatly improves the learning speed."

Kluczowe wnioski z

Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis

by Ruiyang Qin,... o arxiv.org 04-18-2024

https://arxiv.org/pdf/2311.12275.pdf

Głębsze pytania

How can the proposed framework be extended to handle more complex user interactions, such as multi-turn dialogues or task-oriented conversations

To extend the proposed framework to handle more complex user interactions like multi-turn dialogues or task-oriented conversations, several adjustments and enhancements can be implemented:

Contextual Understanding: Incorporate a memory mechanism in the framework to retain context from previous interactions. This will enable the model to understand and respond appropriately in multi-turn dialogues.

Task-Oriented Dialogue Management: Integrate a dialogue management component that can track the progress of a conversation towards a specific goal or task. This will allow the framework to handle task-oriented conversations effectively.

Slot Filling and Intent Recognition: Implement techniques for slot filling and intent recognition to extract relevant information from user inputs in task-oriented dialogues. This will help the model understand user requests and respond accurately.

Dynamic Prompt Generation: Develop a mechanism to dynamically generate prompts based on the context of the conversation. This will guide the LLM in generating relevant responses in multi-turn dialogues.

Fine-Tuning Strategies: Explore fine-tuning strategies that focus on capturing the nuances of multi-turn dialogues and task-oriented conversations. This may involve pre-training the model on specific dialogue datasets or incorporating reinforcement learning techniques.

By incorporating these enhancements, the framework can effectively handle more complex user interactions, providing personalized and contextually relevant responses in various dialogue scenarios.

What are the potential privacy and security implications of generating synthetic data using the LLM, and how can these be addressed

The generation of synthetic data using the LLM raises important privacy and security considerations that need to be addressed:

Data Anonymization: Ensure that any sensitive or personally identifiable information in the synthetic data is anonymized to protect user privacy.

Ethical Guidelines: Adhere to ethical guidelines and regulations regarding data generation and usage to prevent misuse of synthetic data for malicious purposes.

Secure Data Storage: Implement robust security measures to protect the synthetic data generated by the LLM, including encryption and access control mechanisms.

User Consent: Obtain explicit consent from users before using their data to generate synthetic content, ensuring transparency and trust in the data generation process.

Regular Audits: Conduct regular audits and assessments of the synthetic data generation process to identify and mitigate any potential privacy or security risks.

By addressing these considerations and implementing appropriate safeguards, the framework can mitigate privacy and security implications associated with generating synthetic data using the LLM.

How can the framework be adapted to work with different types of large language models, beyond the Llama-3B used in the experiments

Adapting the framework to work with different types of large language models beyond the Llama-3B used in the experiments involves the following steps:

Model Compatibility: Ensure that the framework is compatible with the architecture and specifications of the target large language model. This may involve modifying data processing pipelines and fine-tuning strategies to suit the specific model requirements.

Transfer Learning Techniques: Implement transfer learning techniques to adapt the framework to new large language models. This involves leveraging pre-trained models and fine-tuning them on domain-specific data to achieve optimal performance.

Model Evaluation: Conduct thorough evaluation and testing of the framework with different large language models to assess performance, scalability, and compatibility. This will help identify any model-specific adjustments needed for seamless integration.

Hyperparameter Tuning: Fine-tune hyperparameters of the framework to optimize performance with different large language models. This includes adjusting batch sizes, learning rates, and other parameters based on the specific characteristics of the model.

Model-specific Enhancements: Incorporate model-specific enhancements or modifications to leverage unique features or capabilities of different large language models. This may involve customizing data selection criteria, synthesis techniques, or fine-tuning strategies based on the model architecture.

By following these steps and customizing the framework to suit the requirements of different large language models, it can be effectively adapted to work with a diverse range of models for on-device personalization.