A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical Alignment of Large Language Models

• URL: http://arxiv.org/abs/2502.00136v1
• Date: Fri, 31 Jan 2025 19:41:28 GMT
• Title: A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical Alignment of Large Language Models
• Authors: Edward Y. Chang,
• Abstract summary: This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs) It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation.
• Score: 2.5200794639628032
• License:
• Abstract: This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation. The adversarial DIKE-ERIS duality enables adaptation to diverse cultural contexts while upholding consistent ethical principles. This architecture addresses limitations of reinforcement learning with human feedback (RLHF) by providing interpretable, adaptable, and culturally-aware ethical reasoning. Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.

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