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AI PERSONA: Towards Life-long Personalization of LLMs
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In this work, we introduce the task of life-long personalization of large language models. 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.
Forward citations
Cited by 3 Pith papers
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Synthetic Interaction Data for Scalable Personalization in Large Language Models
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Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation
Fine-tuning on speaker-attributed, action-tagged transcripts from public meetings lets LLM agents mimic government meeting participants well enough that human judges often cannot tell them from real people.
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PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
PersonaFeedback provides a human-labeled benchmark showing current LLMs, including strong reasoners, score only about 65-70 percent on hard personalization choices, and explicit persona information helps more than retrieval.
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