arXiv:2606.02300v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remains an open challenge. Existing approaches predominantly adopt a flat behavioral paradigm, aggregating user behaviors without an explicit account of how they are organized into deeper behavioral structures. In this work, we draw on Pierre Bourdieu's Theory of Practice to propose PHF (Practice-Habitus-Field), a sociologically grounded framework that reconceptualizes LLM personalization through three
The rapid advancement of LLMs necessitates more sophisticated personalization techniques to move beyond basic integration and truly unlock their potential in user-specific applications.
This work proposes a novel, sociologically grounded framework for LLM personalization, offering a more nuanced understanding of user behavior beyond simple aggregations, which could lead to truly adaptive and effective AI.
Current flat behavioral models for LLM personalization are challenged by a hierarchical, context-aware framework, potentially leading to significantly more effective and human-centric AI interactions.
- · AI developers
- · Consumer-facing AI platforms
- · Personalized learning platforms
- · Digital advertisers
- · Providers of generic AI services
- · Systems relying on rudimentary personalization
- · Companies with limited user data
More accurate and deeply personalized LLM outputs will become standard.
This deep personalization could lead to new forms of user engagement and dependency on AI systems.
The ethical implications of deeply personalized AI, including potential manipulation and echo chambers, will become a more pressing societal debate.
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Read at arXiv cs.CL