- The paper introduces PersonalityEdit, a novel benchmark that edits specific personality traits in LLM responses with automated and human-verified methods.
- It compares training-dependent and prompt-based editing techniques, showing that prompt-based methods yield more fluent text generation.
- Experimental evaluations using models like GPT-J and Llama-2 reveal variable success rates, with Agreeableness edits achieving notably higher accuracy.
Editing Personality Traits in LLMs
Introduction
This work introduces an innovative task centered on editing personality traits of LLMs. The task is predicated on adjusting responses to opinionated queries across specified topics, leveraging the notion that individual personality traits often surface through expressed opinions. The paper draws from Social Psychology to identify three primary personality traits—Neuroticism, Extraversion, and Agreeableness—as the focus for their benchmark, known as PersonalityEdit. The data generation process employed GPT-4 to create responses that not only align with specified topics but also manifest the targeted personality trait. This work stands out by meticulously employing automated methods alongside human verification for data quality assurance.
Background
The paper situates itself within the burgeoning field of research into LLMs' role-playing capabilities, pushing the envelope on understanding and enhancing LLM interactions by imbuing them with distinct personality traits. This pursuit stems from recognizing humans' unique personalities, especially as reflected in their reactions and opinions. To construct PersonalityEdit, the authors select personality traits that are both distinct in opinion texts and foundational, based on the Big Five personality model.
Dataset Construction
The benchmark's creation involved:
- Selection of Personality Traits and Facets: Three main personality traits from the Big Five model were selected based on their comprehensibility and distinctiveness in expressing opinions.
- Data Generation: Employing GPT-4 to generate data, the process prioritized topics of high popularity to ensure enriched and high-quality responses. An innovative approach combined automated methods with human verification to maintain data integrity.
Experimental Setup
Using GPT-J and Llama-2 series models, the paper proposes several metrics for evaluating personality traits in generated text, including Edit Success (ES), Drawdown (DD), Accuracy, Target Personality Edit Index (TPEI), and Personality Adjective Evaluation (PAE). The benchmarks evaluated different model editing methods, revealing that while existing approaches can somewhat facilitate personality editing, the task remains notably challenging.
Findings and Discussion
The comprehensive evaluation reveals a nuanced landscape:
- Baseline Comparisons: Training-dependent methods like MEND and SERAC showed potential in logits-level editing but struggled with fluent text generation. Conversely, prompt-based editing approaches demonstrated superior text generation capabilities.
- Inherent LLM Personalities: Initial evaluations indicated that LLMs before editing predominantly exhibited traits of Extraversion and Neuroticism, with Agreeableness being less frequent.
- Analysis of Target Personality Edits: The paper found varying success rates for different target personality edits. Editing for Agreeableness generally showed higher accuracy, suggesting a robust response to personality editing across these models.
Conclusion and Future Work
This paper pioneers the exploration of editing personality traits in LLMs, laying the groundwork for future research in both theoretical and practical domains of AI. The introduced benchmark, PersonalityEdit, grounded in the Big Five personality model, opens new avenues for personalized LLM interactions. The paper speculates on future developments that may enable finer-grained personality edits, advocating for research into improving LLMs' ability to exhibit a broader spectrum of human-like personality traits.
Ethical Considerations
Addressing ethical concerns, the paper underscores the significance of mindful personality editing, cautioning against the inadvertent propagation of biases or offensive content. It calls for responsible utilization and a regulatory framework to preemptively address potential misuse.
Reproducibility and Open Science
In the spirit of open science, the authors commit to releasing the codes and datasets accompanying their research, facilitating further exploration and development in the field by the wider academic community.