User-LLM: Efficient LLM Contextualization with User Embeddings
(2402.13598)Abstract
LLMs have revolutionized natural language processing. However, effectively incorporating complex and potentially noisy user interaction data remains a challenge. To address this, we propose User-LLM, a novel framework that leverages user embeddings to contextualize LLMs. These embeddings, distilled from diverse user interactions using self-supervised pretraining, capture latent user preferences and their evolution over time. We integrate these user embeddings with LLMs through cross-attention and soft-prompting, enabling LLMs to dynamically adapt to user context. Our comprehensive experiments on MovieLens, Amazon Review, and Google Local Review datasets demonstrate significant performance gains across various tasks. Notably, our approach outperforms text-prompt-based contextualization on long sequence tasks and tasks that require deep user understanding while being computationally efficient. We further incorporate Perceiver layers to streamline the integration between user encoders and LLMs, reducing computational demands.
Overview
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The paper introduces User-LLM, a framework enhancing LLM personalization by using user embeddings from interaction data.
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User-LLM employs a Transformer-based encoder for embedding generation and integrates these embeddings with LLMs for personalized outputs.
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The framework shows performance improvements over traditional methods in tasks requiring deep user understanding and handling long sequences.
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Future directions include advancing user embedding generation methods, optimizing integration mechanisms, and expanding applicability across various tasks.
Enhancing LLM Personalization with User Embeddings: Introducing User-LLM
Introduction to User-LLM
Recent advancements in LLMs have paved new pathways in natural language processing, offering unprecedented capabilities in understanding and generating human language. However, leveraging these models to their full potential, especially in the context of personalized user experiences, presents unique challenges. Traditional methods, such as text-prompt-based personalization, struggle with the complexity, noise, and length of real-world user interaction data. Addressing these limitations, we introduce User-LLM, a framework designed to enhance LLMs' personalization capabilities by incorporating user embeddings derived from diverse user interactions.
Framework Overview
User-LLM operates in two primary phases: generating high-quality user embeddings from interaction data and contextualizing LLMs with these embeddings to produce personalized outputs. At its core, User-LLM employs a Transformer-based encoder for embedding generation, capturing latent user preferences across multiple interaction modalities through self-supervised pretraining. These embeddings are then integrated with LLMs through cross-attention mechanisms or soft-prompting, allowing for dynamic adaptation to the user's context. Notably, User-LLM demonstrates substantial performance gains across various tasks, significantly outperforming traditional text-prompt-based methods in scenarios involving long sequences or requiring deep user understanding.
Theoretical Contributions and Practical Implications
The introduction of User-LLM marks a significant step forward in addressing the computational and contextual challenges associated with personalizing LLMs. By distilling user interactions into dense embeddings, User-LLM mitigates the computational burden typically associated with processing long user histories, thus enabling more efficient and effective personalization across a range of applications. The framework's adaptability is further highlighted through its compatibility with various encoder architectures and its ability to handle multimodal user data effectively.
Our comprehensive experiments across the MovieLens, Amazon Review, and Google Local Review datasets have demonstrated User-LLM's superior performance in improving personalized recommendations, user understanding, and content generation. These results underline not only the efficacy of User-LLM in current user modeling scenarios but also its potential to pave the way for new personalized applications and services powered by LLMs.
Future Directions
The exploration of User-LLM opens several avenues for future research. One promising direction lies in advancing the methods for generating user embeddings, potentially through more sophisticated self-supervised pretraining techniques or by exploring new modalities of user data. Additionally, further studies could focus on the integration mechanisms between user embeddings and LLMs, seeking to optimize this process for improved performance and efficiency. Finally, expanding User-LLM's applicability to a wider array of tasks and domains may reveal deeper insights into its versatility and potential as a tool for enhancing personalization in LLMs.
In conclusion, User-LLM represents a significant advance in the field of LLM personalization, offering a novel approach to overcoming the computational and contextual challenges inherent in utilizing LLMs for personalized user experiences. As we continue to explore and refine this framework, the potential for creating more engaging, context-aware, and personalized applications and services is immense.
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