This paper introduces MATRIX, a novel method for self-alignment of large language models (LLMs) through social scene simulation. The core idea is to enable LLMs to simulate realistic social interactions and consequences, allowing them to self-critique and revise their responses to align with human values. MATRIX simulates social scenes by generating roles and interactions, with a social modulator ensuring consistency and governing communication. The LLM then uses these simulations to generate consequence-aware responses, which are further fine-tuned to enhance alignment with human values without compromising inference speed. The method is evaluated against multiple baselines across four benchmarks, showing superior performance in value alignment. The 13B-size LLM tuned with MATRIX outperforms GPT-4 in human evaluations, demonstrating its effectiveness in generating socially aligned responses. Theoretical analysis shows that MATRIX enhances LLM self-alignment by generating more instruction-specific critiques than human-defined ones. The system is designed to be independent of external resources, enabling efficient and cost-effective alignment. MATRIX is compared with existing methods, showing its distinctiveness in generating scenario-specific critiques through social impact simulations. It is also evaluated for its ability to maintain general capabilities while improving alignment. Ablation studies show that increasing the number of agents and interactions improves value alignment, while the composition of training data affects performance. The method is also tested on different LLMs, showing its general applicability. The paper concludes that MATRIX provides a novel approach to aligning LLMs with human values through social scene simulation, achieving superior performance in value alignment while maintaining inference efficiency. The method is expected to contribute to more responsible AI development and ensure AI systems act in ways that are beneficial to society.This paper introduces MATRIX, a novel method for self-alignment of large language models (LLMs) through social scene simulation. The core idea is to enable LLMs to simulate realistic social interactions and consequences, allowing them to self-critique and revise their responses to align with human values. MATRIX simulates social scenes by generating roles and interactions, with a social modulator ensuring consistency and governing communication. The LLM then uses these simulations to generate consequence-aware responses, which are further fine-tuned to enhance alignment with human values without compromising inference speed. The method is evaluated against multiple baselines across four benchmarks, showing superior performance in value alignment. The 13B-size LLM tuned with MATRIX outperforms GPT-4 in human evaluations, demonstrating its effectiveness in generating socially aligned responses. Theoretical analysis shows that MATRIX enhances LLM self-alignment by generating more instruction-specific critiques than human-defined ones. The system is designed to be independent of external resources, enabling efficient and cost-effective alignment. MATRIX is compared with existing methods, showing its distinctiveness in generating scenario-specific critiques through social impact simulations. It is also evaluated for its ability to maintain general capabilities while improving alignment. Ablation studies show that increasing the number of agents and interactions improves value alignment, while the composition of training data affects performance. The method is also tested on different LLMs, showing its general applicability. The paper concludes that MATRIX provides a novel approach to aligning LLMs with human values through social scene simulation, achieving superior performance in value alignment while maintaining inference efficiency. The method is expected to contribute to more responsible AI development and ensure AI systems act in ways that are beneficial to society.