Unlocking Transparent Alignment Through Enhanced Inverse Constitutional AI for Principle Extraction

• URL: http://arxiv.org/abs/2501.17112v1
• Date: Tue, 28 Jan 2025 17:59:56 GMT
• Title: Unlocking Transparent Alignment Through Enhanced Inverse Constitutional AI for Principle Extraction
• Authors: Carl-Leander Henneking, Claas Beger,
• Abstract summary: Constitutional AI (CAI) offers an explicit, rule-based framework for guiding model outputs.<n>We refine the Inverse Constitutional AI (ICAI) algorithm, which extracts constitutions from preference datasets.<n>Our results highlight the potential of these principles to foster more transparent and adaptable alignment methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional methods for aligning Large Language Models (LLMs), such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on implicit principles, limiting interpretability. Constitutional AI (CAI) offers an explicit, rule-based framework for guiding model outputs. Building on this, we refine the Inverse Constitutional AI (ICAI) algorithm, which extracts constitutions from preference datasets. By improving principle generation, clustering, and embedding processes, our approach enhances the accuracy and generalizability of extracted principles across synthetic and real-world datasets. While in-context alignment yields modest improvements, our results highlight the potential of these principles to foster more transparent and adaptable alignment methods, offering a promising direction for future advancements beyond traditional fine-tuning.

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