Tailoring Personality Traits In Large Language Models Via Unsupervisedly-built Personalized Lexicons

Li Tianlong, Dou Shihan, Lv Changze, Liu Wenhao, Xu Jianhan, Wu Muling, Ling Zixuan, Zheng Xiaoqing, Huang Xuanjing. Arxiv 2023

[Paper]    
Fine Tuning Pretraining Methods Prompting Tools Training Techniques

Personality plays a pivotal role in shaping human expression patterns, thus regulating the personality of large language models (LLMs) holds significant potential in enhancing the user experience of LLMs. Previous methods either relied on fine-tuning LLMs on specific corpora or necessitated manually crafted prompts to elicit specific personalities from LLMs. However, the former approach is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address the above challenges, we have employed a novel Unsupervisedly-Built Personalized Lexicons (UBPL) in a pluggable manner during the decoding phase of LLMs to manipulate their personality traits. UBPL is a lexicon built through an unsupervised approach from a situational judgment test dataset (SJTs4LLM). Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLM’s personality.

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