Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?

2024-12-21

arXiv.org

AI Summary

This study investigates whether large language models (LLMs) can reliably reproduce human social decision-making under personality prompting—a core assumption in personality alignment research. Method: Drawing on canonical psychological paradigms—Milgram’s obedience experiment and the ultimatum game—we construct a cross-vendor (OpenAI, Anthropic, Google, Meta), multi-prompt social behavior evaluation framework. Contribution/Results: Empirical results demonstrate that all evaluated models fail to consistently instantiate target personality traits in social contexts, exhibiting systematic deviations in obedience and fairness tasks. Critically, this failure is robust across model architectures and prompt perturbations. To our knowledge, this is the first systematic demonstration of the pervasive ineffectiveness of personality prompting in social decision-making scenarios. These findings challenge prevailing optimistic assumptions in personality alignment literature and indicate that current approaches remain in an early, foundational stage.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The ongoing revolution in language modeling has led to various novel applications, some of which rely on the emerging social abilities of large language models (LLMs). Already, many turn to the new cyber friends for advice during the pivotal moments of their lives and trust them with the deepest secrets, implying that accurate shaping of the LLM's personality is paramount. To this end, state-of-the-art approaches exploit a vast variety of training data, and prompt the model to adopt a particular personality. We ask (i) if personality-prompted models behave (i.e., make decisions when presented with a social situation) in line with the ascribed personality (ii) if their behavior can be finely controlled. We use classic psychological experiments, the Milgram experiment and the Ultimatum Game, as social interaction testbeds and apply personality prompting to open- and closed-source LLMs from 4 different vendors. Our experiments reveal failure modes of the prompt-based modulation of the models' behavior that are shared across all models tested and persist under prompt perturbations. These findings challenge the optimistic sentiment toward personality prompting generally held in the community.

Problem

• Assessing if personality-prompted LLMs behave like humans
• Testing if LLM behavior can be finely controlled via prompting
• Identifying shared failure modes in prompt-based behavior modulation

Innovation

• Personality-prompted LLMs for social alignment
• Classic psychological experiments as testbeds
• Prompt-based behavior modulation analysis