Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization (2409.16973v1)

Abstract: LLMs have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensive. To address these issues, we present Adaptive Self-Supervised Learning Strategies (ASLS), which utilizes self-supervised learning techniques to personalize LLMs dynamically. The framework comprises a user profiling layer for collecting interaction data and a neural adaptation layer for real-time model fine-tuning. This innovative approach enables continuous learning from user feedback, allowing the model to generate responses that align closely with user-specific contexts. The adaptive mechanisms of ASLS minimize computational demands and enhance personalization efficiency. Experimental results across various user scenarios illustrate the superior performance of ASLS in boosting user engagement and satisfaction, highlighting its potential to redefine LLMs as highly responsive and context-aware systems on-device.

• The paper introduces the ASLS framework, a dual-layer system that dynamically personalizes LLMs without extensive labeled data.
• It employs a user profiling layer to capture real-time interactions and a neural adaptation layer to fine-tune model parameters continuously.
• Experimental results demonstrate that ASLS significantly enhances user engagement and response relevance over conventional methods.

Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization

The paper "Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization" introduces a novel framework called Adaptive Self-Supervised Learning Strategies (ASLS) designed to dynamically personalize LLMs on user devices. This approach addresses several challenges inherent in traditional model personalization, especially the dependence on labeled data and significant computational resources.

Summary and Key Contributions

The ASLS framework incorporates two core components: a user profiling layer and a neural adaptation layer. The user profiling layer collects and processes interaction data, while the neural adaptation layer fine-tunes the model in real-time based on the collected data. This dual-layer structure allows continuous learning from user feedback, which enhances the model's ability to produce responses that are closely aligned with user-specific contexts and needs. The primary contributions of the paper are as follows:

• Introduction of the ASLS Framework: A self-supervised learning framework that dynamically personalizes LLMs without requiring extensive labeled data.
• User Profiling and Neural Adaptation: The integration of a user profiling layer and a neural adaptation layer allows for real-time model fine-tuning based on dynamically captured user data, enhancing adaptability and responsiveness.
• Experimental Validation: Comprehensive experiments conducted across various user scenarios demonstrate that ASLS significantly enhances user engagement and satisfaction compared to traditional personalization methods.

Methodology

The ASLS framework operates through a continuous learning process facilitated by the two core layers:

1. User Profiling Layer: This layer captures user interaction data and constructs user profiles, representing individual preferences. The user interaction data is modeled as a set $D = \{d_1, d_2, ..., d_T\}$, where each $d_t$ represents interactions over time. Features are extracted from this data to create user embeddings $\mathbf{u_t}$.
2. Neural Adaptation Layer: The neural adaptation layer leverages these user embeddings to update the model's parameters in real-time. This adaptive fine-tuning ensures the model continuously aligns with the user's evolving preferences, formalized as:

   $\theta' = \theta + \Delta\theta(\mathbf{u_t}),$

   where $\Delta\theta(\mathbf{u_t})$ is the parameter adjustment based on user embeddings.

The overall objective is to minimize the loss $L(\theta)$ across user interactions, optimizing the model's parameters to improve response accuracy and relevance.

Experimental Results

The paper's experimental validation of ASLS involved several datasets and user scenarios, illustrating significant performance improvements. For instance, the Llama-3-7b model using ASLS achieved an average engagement score of 82.7, outperforming conventional methods. The experimental setup highlighted the versatility and efficiency of ASLS in dynamically tailoring LLM responses.

Additionally, ablation studies underscored the importance of both user profiling and neural adaptation layers. Scenarios where either component was omitted showed a marked decline in performance, reinforcing the necessity of an integrated approach.

Implications and Future Developments

The implications of ASLS are substantial for the development of more context-aware and responsive LLMs. By reducing dependence on labeled datasets and optimizing computational resources, ASLS paves the way for scalable and efficient personalization of LLMs on user devices. This approach can significantly enhance user engagement and satisfaction across various applications, from healthcare to education.

Future research could explore several directions:

• Enhanced Data Collection: Improving methods for collecting and processing user interaction data to enrich user profiles.
• Resource Optimization: Further optimizing the computational aspects of real-time adaptation, particularly for resource-constrained devices.
• Ethical Considerations: Addressing ethical concerns related to user privacy and the fairness of personalized responses.

Conclusion

The Adaptive Self-Supervised Learning Strategies framework represents a significant step towards efficient, dynamic personalization of LLMs. By leveraging self-supervised learning to continuously adapt to user feedback, ASLS has demonstrated its potential to transform LLMs into highly personalized, context-aware systems, thereby improving the overall on-device user experience.