From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs

Navya Jain, Zekun Wu, CRISTIAN ENRIQUE MUNOZ VILLALOBOS, Airlie Hilliard, Adriano Koshiyama, Emre Kazim, Philip Colin Treleaven

Keywords: Large Language Models, Personality Manipulation, Finetuning, Emoji Generation, OCEAN Model, AI Personality, Benchmarking, AI Behavioral Modeling, Emergent Behavior in AI

TL;DR: This paper shows how Parameter-Efficient Fine-Tuning (PEFT) manipulates Big Five personality traits in LLMs more consistently than prompt-based methods, leading to spontaneous emoji generation as an expression of these traits.

Abstract: As the demand for human-like interactions with LLMs continues to grow, so does the interest in manipulating their personality traits, which has emerged as a key area of research. Methods like prompt-based In-Context Knowledge Editing (IKE) and gradient-based Model Editor Networks (MEND) have been explored but show irregularity and variability. IKE depends on the prompt, leading to variability and sensitivity, while MEND yields inconsistent and gibberish outputs. To address this, we employed Opinion QA Based Parameter-Efficient Fine-Tuning (PEFT), specifically Quantized Low-Rank Adaptation (QLoRA), to manipulate the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. After PEFT, models such as Mistral-7B-Instruct and Llama-2-7B-chat began generating emojis, despite their absence in the PEFT data. For instance, Llama-2-7B-chat generated emojis in 99.5\% of extraversion-related test instances, while Mistral-7B-Instruct did so in 92.5\% of openness-related test instances. Explainability analysis indicated that the LLMs used emojis intentionally to express these traits. This paper provides a number of novel contributions. First, introducing an Opinion QA dataset for PEFT-driven personality manipulation; second, developing metric models to benchmark LLM personality traits; third, demonstrating PEFT's superiority over IKE in personality manipulation; and finally, analysing and validating emoji usage through explainability methods such as mechanistic interpretability and in-context learning explainability methods.

Submission Number: 10