This study explores the consistency of personality traits in quantized large language models (LLMs) for edge device role-playing scenarios. Using the Big Five personality traits model, we evaluate how stable assigned personalities are for Quantized Role-Playing Dialog Agents (QRPDA) during multi-turn interactions. We evaluate multiple LLMs with various quantization levels, combining binary indexing of personality traits, explicit self-assessments, and linguistic analysis of narratives. To address personality inconsistency, we propose a non-parametric method called Think2. Our multi-faceted evaluation framework demonstrates Think2’s effectiveness in maintaining consistent personality traits for QRPDA. Moreover, we offer insights to help select the optimal model for QRPDA, improving its stability and reliability in real-world applications.

Large Language Models (LLMs) have made significant progress, becoming more autonomous and capable of handling real-world tasks through their access to tools, various planning strategies, and memory, referred to as LLM agents. One emerging area of focus is customizing these models to cater to individual user preferences, thereby shaping them into personal LLM agents. This work investigates how the user model, which encapsulates user-related information, preferences, and personal concepts, influences an LLM agent’s planning and reasoning capabilities. We introduce a personalized version of TravelPlanner, called TravelPlanner+, and establish baselines for personal LLM agents. Our evaluation strategy contains an LLM-as-a-Judge component, which provides further in-depth insights into the decision-making process of a personal LLM agent by comparing generic and personal plans. Our findings reveal that while generic plans perform robustly, personal plans show marked improvement in relevance and suitability, with preference rates up to 74.4% on validation and 87.3% on the test set. These results highlight the potential of personal LLM agents to significantly enhance user satisfaction.