Multi-Personality Generation of LLMs at Decoding-time

• URL: http://arxiv.org/abs/2511.01891v1
• Date: Mon, 27 Oct 2025 09:45:11 GMT
• Title: Multi-Personality Generation of LLMs at Decoding-time
• Authors: Rongxin Chen, Yunfan Li, Yige Yuan, Bingbing Xu, Huawei Shen,
• Abstract summary: Multi-personality generation for LLMs is a fundamental challenge.<n>Existing approaches are costly and poorly scalable.<n>We propose a novel Multi-Personality Generation framework under the decoding-time combination paradigm.
• Score: 34.04566617442129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-personality generation for LLMs, enabling simultaneous embodiment of multiple personalization attributes, is a fundamental challenge. Existing retraining-based approaches are costly and poorly scalable, while decoding-time methods often rely on external models or heuristics, limiting flexibility and robustness. In this paper, we propose a novel Multi-Personality Generation (MPG) framework under the decoding-time combination paradigm. It flexibly controls multi-personality without relying on scarce multi-dimensional models or extra training, leveraging implicit density ratios in single-dimensional models as a "free lunch" to reformulate the task as sampling from a target strategy aggregating these ratios. To implement MPG efficiently, we design Speculative Chunk-level based Rejection sampling (SCR), which generates responses in chunks and parallelly validates them via estimated thresholds within a sliding window. This significantly reduces computational overhead while maintaining high-quality generation. Experiments on MBTI personality and Role-Playing demonstrate the effectiveness of MPG, showing improvements up to 16%-18%. Code and data are available at https://github.com/Libra117/MPG .

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