AI PERSONA: Towards Life-long Personalization of LLMs (2412.13103v1)

Abstract: In this work, we introduce the task of life-long personalization of LLMs. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language agents, to continuously adapt to the diverse and ever-changing profiles of every distinct user and provide up-to-date personalized assistance. We provide a clear task formulation and introduce a simple, general, effective, and scalable framework for life-long personalization of LLM systems and language agents. To facilitate future research on LLM personalization, we also introduce methods to synthesize realistic benchmarks and robust evaluation metrics. We will release all codes and data for building and benchmarking life-long personalized LLM systems.

AI Persona: Towards Life-long Personalization of LLMs

The paper introduces a novel framework for achieving life-long personalization in LLMs, addressing a crucial gap in developing generalized intelligence capable of continuously adapting to individuals' dynamic preferences and identities. The authors propose AI Persona, a framework designed to enable LLMs to maintain personalized interaction histories while adapting to users' evolving profiles. This framework encompasses several key innovations in data synthesis, benchmarking, and system architecture aimed at enhancing user satisfaction and aligning responses with personalized context.

In the current computational landscape, substantial resources are deployed towards enhancing the capabilities of LLMs through scaling data and computational resources. However, there is a lack of focus on how advanced LLMs can cater to personalized, changing profiles based on long-term user interaction histories. AI Persona seeks to fill this gap by integrating user personas into LLMs to tailor responses meaningfully, with implications for enhancing user satisfaction in interaction with AI systems.

Key contributions of the paper include:

1. Life-Long Personalization Definition: The authors provide a structured conceptualization of life-long personalization, emphasizing the importance of constant adaptation in understanding and predicting user needs. To date, LLM personalization methodologies have largely been confined to one-time changes or static profiles without accounting for continuous learning and adaptation.
2. Benchmark Introduction - PersonaBench: The paper introduces PersonaBench, a new benchmark built to assess LLM personalization. It comprises diverse, realistically synthesized personas and user-agent interaction scenarios. This ensures that the models are tested in environments reflecting more genuine, diverse user interactions than the existing LaMP benchmark, which the authors argue does not adequately capture the complexity and specificity of real-world user-agent exchanges.
3. Framework for Personalized LLM Systems: AI Persona is outlined as a scalable, adaptable system that models user profiles as learnable dictionaries. These profiles evolve through continuous feedback from user interactions, with the system dynamically assembling user personas during LLM operations to ensure personalized, contextually relevant responses.
4. Empirical Evaluation: Empirical analysis demonstrates the efficacy of their approach, showing significant improvements in aligning LLM outputs to user personas and handling ongoing contextual shifts in user profiles. The framework efficiently scales to support substantial user bases without necessitating costly and frequent retraining of the entire model.

The implications of this research extend both theoretically and practically. Theoretically, it posits a paradigm shift in how personalization is approached in LLMs, suggesting the incorporation of dynamic user models rather than static personalization parameters. Practically, the integration of such frameworks could lead to enhanced user experiences, more accurate and empathetic AI interactions, and potentially wider adoption of AI in contexts necessitating personalized care or attention, such as mental health support or personal assistants.

Future developments could further explore the potential for integrating these personas with additional modalities, like contextual or sensory data, thereby expanding the landscape of personalized AI interactions. The eventual goal, as outlined in the paper, is a generation of AI systems that are not only technically proficient but also possess an understanding and adaptation to the intricate details of human individualities and real-world dynamics.