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UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation

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arxiv 2310.16582 v3 pith:2OTOQZL7 submitted 2023-10-25 cs.CL

classification cs.CL
keywords llmspersonalityfine-grainedpersonalitiesfashionlanguagelargemanipulate
verification ladder T0 review T1 audit T2 compute T3 formal
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Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs) holds significant potential in enhancing their user experiences. Previous approaches either relied on fine-tuning LLMs on specific corpora or required manually crafted prompts to evoke specific personalities from LLMs. However, the former is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address these challenges, we propose UPLex, a method that uses an Unsupervisedly-Built Personalized Lexicon (UPL) during the decoding phase to manipulate LLM's personality traits. UPL can be constructed from a newly built situational judgment test dataset in an unsupervised fashion, and used to modulate the personality expression of LLMs by dynamically altering their predicted probability of upcoming words in a pluggable fashion. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLMs' personalities.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Can LLM "Self-report"?: Evaluating the Validity of Self-report Scales in Measuring Personality Design in LLM-based Chatbots

    cs.HC 2024-11 conditional novelty 6.0 of 10

    Chatbot self-report personality scores correlate only weakly with human-perceived personality and interaction quality across 500 GPT-4o chatbots, undermining the validity of self-report scales in this context.

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