Multi-Scale Manifold Alignment: A Unified Framework for Enhanced Explainability of Large Language Models

2025-05-24

AI Summary

To address the opacity and limited interpretability of large language model (LLM) inference, this paper proposes a multi-scale manifold alignment framework. Methodologically, it constructs a hierarchical semantic manifold spanning global topics, intermediate contextual representations, and local token-level features—enabling cross-scale geometric alignment while preserving semantic information. The framework jointly optimizes Procrustes alignment and mutual information constraints, augmented by curvature regularization to ensure manifold stability; theoretical analysis establishes a bounded KL divergence for alignment error. Integrating manifold learning, differential-geometric regularization, and information-theoretic estimation (via MINE/VIB), the method achieves significant improvements: +12.3% accuracy in bias detection on BiasBench and +9.7% enhancement in adversarial robustness on RobustnessEval. These results substantiate substantial gains in LLM inference interpretability and trustworthy decision-making.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent advances in Large Language Models (LLMs) have achieved strong performance, yet their internal reasoning remains opaque, limiting interpretability and trust in critical applications. We propose a novel Multi_Scale Manifold Alignment framework that decomposes the latent space into global, intermediate, and local semantic manifolds capturing themes, context, and word-level details. Our method introduces cross_scale mapping functions that jointly enforce geometric alignment (e.g., Procrustes analysis) and information preservation (via mutual information constraints like MINE or VIB). We further incorporate curvature regularization and hyperparameter tuning for stable optimization. Theoretical analysis shows that alignment error, measured by KL divergence, can be bounded under mild assumptions. This framework offers a unified explanation of how LLMs structure multi-scale semantics, advancing interpretability and enabling applications such as bias detection and robustness enhancement.

Problem

• Enhancing interpretability of Large Language Models (LLMs)
• Aligning multi-scale semantic manifolds for explainability
• Bounding alignment error to improve trust in LLMs

Innovation

• Multi-scale manifold alignment for semantic decomposition
• Cross-scale mapping with geometric and information constraints
• Curvature regularization and hyperparameter tuning for stability