A Dual-Structure Approach Interpretability System for Large Language Models

Abstract

Large Language Models (LLMs) have revolutionized natural language processing through their remarkable ability to learn from vast corpora. However, the inner workings of these models remain elusive, as the knowledge they acquire is embedded implicitly across billions of parameters. This opacity challenges transparency, explainability, trust, and model improvement. We propose a dual-structure approach that augments the neural network with an auxiliary system designed to continuously extract and structure the model's learned knowledge into human-interpretable formats during training. We discuss four potential implementations—knowledge graph extraction, symbolic representation layers, concept bottleneck layers, and explanatory output generation—examining their relative benefits, challenges, and actionable research directions. Our aim is to transform LLM interpretability, paving the way for AI systems that are not only powerful but also transparent and accountable.

1. Introduction

Modern LLMs have demonstrated impressive capabilities by encoding vast amounts of linguistic and factual knowledge within their distributed representations. However, despite their prowess, the implicit nature of these representations remains a black box, challenging our ability to understand and trust how these models work. Current interpretability efforts are predominantly post-hoc, attempting to reverse engineer decision processes after the fact. Such methods often fall short of providing actionable insights.

In response, we advocate for shifting interpretability upstream. Our proposed dual-structure approach integrates an auxiliary interpretability system directly into the training process, concurrently capturing structured knowledge as it is learned. This framework aims to preserve the predictive flexibility of neural networks while offering insights that are accessible to humans—ranging from data scientists to non-expert users.

2. Related Work

Our work builds upon and contributes to several active research areas:

• Mechanistic Interpretability: This field aims to understand the internal computations of neural networks, particularly transformers. Key works include Olah et al. (2020) on "Zoom In: An Introduction to Circuits," Anthropic's "A Mathematical Framework for Transformer Circuits" (2023), and Meng et al. (2022) on "Locating and Editing Factual Associations in GPT." Our approach differs by focusing on extracting human-understandable representations during training, rather than analyzing trained networks.

• Neuro-Symbolic AI: This area seeks to combine the strengths of neural networks and symbolic reasoning. Relevant works include Garcez et al. (2022) on "Neurosymbolic AI: The 3rd Wave" and Garnelo and Shanahan (2019) on "Reconciling deep learning with symbolic artificial intelligence." Our symbolic representation layer aligns with this goal, but we integrate it directly into the LLM training process.

• Concept Bottleneck Models: This approach forces models to represent information through predefined concepts. Key papers include Kim et al. (2018) on "Interpretability Beyond Feature Attribution (TCAV)" and Koh et al. (2020) on "Concept Bottleneck Models." We extend this to NLP and explore its integration with other interpretability methods.

• Explainable AI (XAI): This broad field includes post-hoc explanation methods like LIME (Ribeiro et al., 2016) and SHAP (Lundberg and Lee, 2017). Our work contrasts with these by aiming for inherent interpretability, built into the model's architecture and training process.

• Knowledge Graph Embeddings: Research on representing knowledge graphs using embeddings (e.g., TransE by Bordes et al., 2013; Yang et al., 2014) is relevant to our knowledge graph extraction component. We leverage these techniques but focus on dynamically constructing the graph from an LLM. We will utilize datasets like ConceptNet, WordNet and ATOMIC to provide a set of possible relations.

3. Motivation: Why Interpretability Matters

The opacity inherent in LLMs limits our ability to verify, trust, and improve these systems. Key challenges include:

• Transparency: Gaining insights into what a model knows, and more importantly, how it organizes that knowledge, demystifies AI reasoning.

• Explainability: Structured internal representations can elucidate the decision pathways behind model predictions, enabling better debugging and trust calibration.

• Trust: An interpretable model increases confidence by exposing potential biases and gaps in the training data or reasoning process.

• Model Improvement: By uncovering inefficiencies or misunderstandings in the training process, structured insights can guide refinements and promote more robust learning.

These challenges underscore the urgency for integrating transparency directly into AI systems, especially in high-stakes applications and under emerging regulatory standards for explainable AI.

4. Proposed Methodology: The Dual-Structure Approach

We propose coupling the conventional neural network with an auxiliary system that extracts and formats learned knowledge in real-time. This dual-structure design, illustrated in Figure 1, allows the network to continue benefitting from flexible, distributed representations, while simultaneously providing interpretable insights for external validation and human oversight.

(Figure 1: Dual-Structure Architecture)

                +------------------+       +----------------------------------+
                | Large Language   |      |      Auxiliary Interpretability   |
                |    Model (LLM)   |      |               System              |
                +------------------+      +-----------------------------------+
                |                  |      |                                   |
Input Text ---> |   Transformer    | ---> |  Knowledge Graph Extraction       |  --->  Knowledge Graph
                |       Layers     | ---> |  Symbolic Representation Layer    |  --->  Symbolic Rules
                |                  | ---> |  Concept Bottleneck Layer         |  --->  Concept Activations
                |                  | ---> |  Explanatory Output Generation    |  --->  Natural Language Explanations
                +------------------+       +----------------------------------+
                |                  |
Output Text <---+                  |
                |                  |
    

Caption: The dual-structure architecture. The LLM (e.g., a transformer) processes input text and generates output text. Simultaneously, the auxiliary system extracts and structures learned knowledge into various human-interpretable formats.

The following four implementations illustrate potential routes for realizing this approach:

4.1 Knowledge Graph Extraction

• Concept: Design a system that dynamically constructs a knowledge graph to map entities and relationships—e.g., inferring triplets like "dog → is-a → animal"—from neural activations or attention distributions during training. Entity recognition can be performed using named entity recognition (NER) techniques, while relation extraction can leverage attention weights between entity pairs or employ relation-specific classifiers trained alongside the main LLM. We will also investigate using existing knowledge graphs (Wikidata, ConceptNet) to guide the extraction process.

• Benefits:

  • Intuitive Visualization: Graphs can offer intuitive visual representations of relational structures.

  • Direct Querying: Enables direct interrogation of the model's memorized relationships.

• Challenges:

  • Distributed Representations: Neural activations are inherently distributed; translating them into discrete graph components can oversimplify underlying complexities.

  • Fidelity Assurance: Ensuring that the extracted graphs maintain fidelity to the model's internal processes is non-trivial.

  • "Curse of Dimensionality": The resulting knowledge graph could be extremely large. We will explore graph summarization, hierarchical knowledge graphs, and on-demand graph expansion to address this.

• Next Steps:

  • Prototype on scaled-down models using curated datasets.

  • Develop quantitative metrics to compare graph-derived insights with the model's raw output patterns (Precision, Recall, F1-score, Link Prediction AUC-ROC).

4.2 Symbolic Representation Layer

• Concept: Introduce a dedicated layer that maps neural patterns to explicit symbolic representations or logical rules (e.g., "if context = medical, then prioritize technical terminology"). We will explore representing learned knowledge using first-order logic, production rules, and semantic networks. The symbolic layer will be trained jointly with the LLM, using a combined loss function that rewards both accurate text prediction and the generation of valid and informative symbolic rules.

• Benefits:

  • Hybrid Approach: Bridges the gap between sub-symbolic neural computations and explicit symbolic reasoning.

  • Interpretability: Offers a direct window into decision rules that are easy for humans to comprehend.

• Challenges:

  • Rigidity vs. Flexibility: Converting nuanced probabilistic reasoning into discrete rules can risk oversimplification and reduced adaptability.

  • Integration Complexity: Balancing the dual outputs of symbolic and traditional predictions may affect overall model performance.

• Next Steps:

  • Develop and assess dual-output architectures that yield both raw predictions and symbolic summaries.

  • Systematically evaluate trade-offs between interpretability and predictive performance (Rule Accuracy, Rule Coverage, Rule Complexity).

4.3 Concept Bottleneck Layer

• Concept: Constrain the model with intermediate bottleneck layers that force representations to pass through predefined interpretable concepts (e.g., sentiment, causality, agency). Concepts will be represented using a learned embedding space, allowing for both discrete and continuous representations of conceptual knowledge. We will enforce the bottleneck constraint using a combination of L1 regularization on the bottleneck activations and the information bottleneck principle, encouraging the model to represent information compactly and efficiently.

• Benefits:

  • Concept Anchoring: Provides clear handles for probing and understanding the model's reasoning processes.

  • Systematic Analysis: Facilitates targeted testing and verification of specific conceptual understandings.

• Challenges:

  • Predefined Bias: Limiting representations to human-defined concepts may constrain the emergence of novel insights.

  • Performance Impact: Overly strict bottlenecks might lead to degradation in model performance.

• Next Steps:

  • Adapt methodologies from vision-based concept bottleneck research to NLP tasks.

  • Begin with a narrow selection of core concepts to minimize constraint-induced performance issues (Concept Classification Accuracy, Bottleneck Activation Sparsity).

4.4 Explanatory Output Generation

• Concept: Train models not only to generate predictions but also to produce natural language explanations that justify their decisions (e.g., “I selected 'cat' due to nearby context clues like ‘paws’ and ‘meow’”). We will focus on generating both free-form natural language explanations and structured explanations that highlight the specific input tokens that most influenced the model's prediction. The explanation generation component will be trained using a combination of supervised learning on a dataset of human-annotated explanations and reinforcement learning, where the reward signal is based on the quality and faithfulness of the generated explanations.

• Benefits:

  • Direct Communication: Aligns with human conversational patterns and eases non-expert interpretability.

  • Complementary Insight: Provides a verbal account that may uncover latent decision paths.

• Challenges:

  • Rationalization Risk: Generated explanations might become post-hoc rationalizations that only partially reflect real internal processes.

  • Dual-Objective Complexity: Balancing prediction performance with explanatory clarity introduces a multi-objective training challenge.

• Next Steps:

  • Fine-tune existing LLMs to be dual-task learners, producing both predictions and aligned explanations.

  • Develop validation metrics to assess the congruence between generated explanations and underlying attention mechanisms (BLEU, ROUGE, METEOR, Human Evaluation - Fluency, Faithfulness, Informativeness).

5. Challenges and Considerations

Implementing a dual-structure approach raises several pivotal questions:

Challenge	Description	Potential Mitigations	Specific Metrics
Human Understandability	Interpretation needs vary widely from experts to laypersons, affecting how insights are presented.	Develop multi-modal interfaces that integrate visual graphs, symbolic logic, and natural language explanations, customizable by user expertise level.	User studies to assess comprehension and preference for different interface modalities.
Fidelity vs. Simplicity	Rich, nuanced internal representations may be degraded when simplified into structured formats.	Employ hierarchical extraction methods that preserve detail while allowing users to toggle between high-level overviews and detailed views.	Correlation between detailed and simplified representations; information loss metrics.
Computational Overhead	Auxiliary interpretability systems could significantly extend training times.	Optimize extraction processes through incremental updates, sparse activation sampling, concurrent lightweight proxy models, model parallelism, pipeline parallelism, data parallelism, and tensor parallelism.	Training time, inference time, memory usage.
Validation and Ground Truth	Directly validating the accuracy of extracted structures against truly internal states is challenging.	Use predictive consistency checks and cross-validation against known benchmarks, refining extraction techniques iteratively to improve reliability.	Knowledge Graph: Precision, Recall, F1, Link Prediction AUC-ROC. Symbolic Layer: Rule Accuracy, Coverage, Complexity.
Intervention-based Evaluation	Understanding the causal effect of the extracted knowledge representations.	Modify extracted knowledge structures and measure the impact on model's output on related tasks.	Change in model output after knowledge intervention (e.g., accuracy drop on related tasks).
			Concept Bottleneck: Concept Classification Accuracy, Sparsity. Explanatory Output: BLEU, ROUGE, METEOR, Human Evaluation.

6. Actionable Roadmap

To operationalize this research agenda, we propose the following steps:

1. Scope Narrowing: Begin with easily identifiable patterns—such as factual relationships or simple reasoning chains—to establish proof-of-concept reliability.

2. Rapid Prototyping: Leverage open-source models integrated with experimental interpretability modules on controlled datasets. This will facilitate quick iteration cycles.

3. Integration of Prior Insights: Incorporate findings from recent work on mechanistic interpretability and neuro-symbolic AI, including transformer circuit analysis and concept bottleneck methodologies, to build a robust theoretical foundation.

4. Iterative Testing and Refinement: Develop quantitative "agreement scores" comparing the structured representations with original neural outputs. This feedback loop will guide continuous refinement of extraction methods.

7. Scalability Studies

Detailed studies on the computational and practical scalability of dual-structure methods across different model sizes and domains should be done. These would involve using techniques like model parallelism, pipeline parallelism, data parallelism, and tensor parallelism to manage the increased computational demands. Reports are needed on training time, inference time, memory usage, and the impact on model performance as we scale up the size and complexity of the LLM and the auxiliary system.

8. Part 1 Conclusion

The black box problem of modern LLMs presents significant obstacles to transparency, accountability, and systematic improvement. Our dual-structure approach—integrating an auxiliary system that extracts human-interpretable knowledge during training—represents a promising step toward bridging this gap. Although challenges such as fidelity, computational cost, and integration complexity remain, the potential benefits in trust, debugging, and regulatory compliance are substantial.

By advancing targeted prototypes in knowledge graph extraction, symbolic reasoning, concept bottlenecking, and explanatory output generation, we hope to inspire further research and ultimately cultivate AI systems that are not only highly capable but also intrinsically transparent and accountable. The future of AI lies not merely in generating answers but in illuminating the underlying thought processes—and in this openness, we find the path to innovation.

Part 1 of 2 concluded.

Part 2 of 2:

AI Systems Required for Dual-Structure Interpretable LLMs

This document outlines the specific AI systems and components needed to implement the dual-structure approach to interpretable Large Language Models as described in the research paper "A Dual-Structure Approach Interpretability System for Large Language Models."

Core AI Components

1. Foundation LLM System

The base of the dual-structure approach requires a fully functional Large Language Model:

• Transformer Architecture: Implementation of a multi-layer transformer model with self-attention mechanisms.
• Pre-training System: Infrastructure for training on large-scale text corpora.
• Fine-tuning Capabilities: Systems for adapting the base model to specific tasks while maintaining interpretability.
• Distributed Training Framework: For handling the computational demands of training large models.
• Parameter Efficient Training Methods: To manage the additional computational overhead of the interpretability systems.

2. Knowledge Graph Extraction System

• Named Entity Recognition (NER) Module: AI for identifying entities within text and model activations.
• Relation Extraction System: AI capable of inferring relationships between identified entities.
• Knowledge Graph Construction Engine: System to dynamically build and update knowledge graphs.
• Attention Analysis System: AI that can interpret attention patterns between tokens to infer relationships.
• Graph Summarization AI: To address the "curse of dimensionality" by condensing large graphs.
• Hierarchical Knowledge Organization: AI for structuring knowledge at different levels of abstraction.

3. Symbolic Representation System

• Neural-to-Symbolic Mapping Engine: AI system that converts distributed neural representations to discrete symbolic forms.
• First-Order Logic Generation: AI capable of producing logical rules from model behaviors.
• Dual-Objective Training System: Framework for optimizing both predictive performance and symbolic representation quality.
• Rule Inference Engine: System to generate and validate logical rules from model activations.
• Rule Simplification AI: For converting complex neural patterns into human-readable rules.
• Rule Consistency Verification: AI to ensure logical consistency across extracted rules.

4. Concept Bottleneck System

• Concept Embedding Space: AI system for representing predefined interpretable concepts.
• Bottleneck Constraint Enforcement: Mechanisms like L1 regularization and information bottleneck principles.
• Concept Activation Vectors: Implementation of techniques like TCAV (Testing with Concept Activation Vectors).
• Sparse Concept Activation: AI for ensuring only relevant concepts are activated.
• Concept Hierarchy System: For organizing concepts into meaningful taxonomies.
• Concept Coverage Analysis: AI to evaluate whether the predefined concepts adequately capture model knowledge.

5. Explanatory Output Generation System

• Natural Language Explanation Generation: AI specifically trained to produce human-readable explanations.
• Explanation Faithfulness Verification: System to ensure explanations accurately reflect model reasoning.
• Multi-Task Learning Framework: For balancing prediction quality with explanation quality.
• Reinforcement Learning Component: To optimize explanations based on quality and faithfulness metrics.
• Attention Visualization System: For highlighting which input elements influenced the prediction.
• Explanation Templates: Customizable frameworks for different types of explanations.

Integration and Evaluation Systems

1. Cross-System Integration Framework

• Unified Training Pipeline: System for jointly training the LLM and interpretability components.
• Communication Interfaces: For data exchange between the main model and auxiliary systems.
• Resource Allocation AI: To dynamically balance computational resources across components.
• Incremental Update System: For efficiently updating interpretability structures during training.

2. Evaluation and Validation Systems

• Fidelity Assessment AI: To measure how accurately the interpretability structures reflect the model's internal processes.
• Interpretability Metrics Framework: System for quantifying interpretability across multiple dimensions.
• Human-AI Collaborative Evaluation: Tools for human experts to assess interpretability quality.
• Intervention-based Testing: Systems for modifying extracted knowledge and measuring effects on model behavior.
• Benchmark Testing Framework: For standardized evaluation across different interpretability approaches.

3. User Interface and Visualization

• Multi-modal Visualization System: AI for rendering different interpretability formats (graphs, rules, text).
• User Adaptation AI: To customize presentations based on user expertise.
• Interactive Exploration Tools: For users to navigate and query the interpretability structures.
• Explainability Dashboards: Integrated interfaces for accessing different interpretability layers.
• Uncertainty Visualization: Systems to communicate confidence levels in interpretations.

Computational Optimization Systems

1. Efficiency Enhancement

• Model Parallelism: Systems for distributing model components across hardware.
• Pipeline Parallelism: For efficient sequential processing of different model stages.
• Data Parallelism: For processing multiple batches simultaneously.
• Tensor Parallelism: For distributing tensor computations.
• Sparse Activation Sampling: AI for selectively processing only the most informative activations.
• Proxy Model Systems: Lightweight models that approximate full model behavior for faster interpretability.

2. Privacy and Security

• Differential Privacy Implementation: To prevent leakage of training data through interpretability structures.
• Adversarial Defense Systems: To prevent manipulation of interpretability components.
• Federated Learning Integration: For privacy-preserving knowledge extraction.
• Bias Detection AI: To identify and mitigate biases in extracted knowledge structures.

Research and Development Infrastructure

• Rapid Prototyping Framework: For quickly testing different interpretability approaches.
• Benchmarking Infrastructure: For comparing performance across different implementations.
• Cross-disciplinary Collaboration Tools: To facilitate work between AI researchers, UX specialists, and domain experts.
• Documentation Generation: AI systems for maintaining clear documentation of complex interpretability structures.
• Version Control for Knowledge Structures: To track evolution of interpretability components during training.

Scaling and Deployment Systems

• Enterprise Integration Framework: For deploying interpretable models in production.
• Regulatory Compliance Systems: To ensure models meet emerging XAI (Explainable AI) standards.
• Domain Adaptation Tools: For customizing interpretability approaches to specific industries.
• Continuous Learning Infrastructure: For updating interpretability structures as models evolve.
• Model Distillation Systems: For creating smaller, more efficient interpretable models.

Implementation: AI Systems and Components

Implementing the dual-structure approach requires a comprehensive suite of AI systems and components, working in concert. This section details the necessary infrastructure.

5.1 Core AI Components

5.1.1 Foundation LLM System

- Transformer Architecture: A multi-layer transformer model with self-attention mechanisms.
- **Pre-training System:** Infrastructure for large-scale text corpora training.
- **Fine-tuning Capabilities:** Systems for task-specific adaptation while preserving interpretability.
- **Distributed Training Framework**: For handling the computational demands of training large models.
- **Parameter Efficient Training Methods**: To manage the additional computational overhead of the interpretability systems.
    

5.1.2 Knowledge Graph Extraction System

- **Named Entity Recognition (NER) Module:** AI for identifying entities within text and model activations.
- **Relation Extraction System:** AI for inferring relationships between entities.
- **Knowledge Graph Construction Engine:** Dynamically builds and updates knowledge graphs.
- **Attention Analysis System:** Interprets attention patterns to infer relationships.
- **Graph Summarization AI:** Condenses large graphs to address the "curse of dimensionality."
- **Hierarchical Knowledge Organization:** Structures knowledge at multiple abstraction levels.
    

5.1.3 Symbolic Representation System

- **Neural-to-Symbolic Mapping Engine:** Converts distributed neural representations to discrete symbolic forms.
- **First-Order Logic Generation:** Produces logical rules from model behaviors.
- **Dual-Objective Training System:** Optimizes predictive performance and symbolic representation quality.
- **Rule Inference Engine:** Generates and validates logical rules from model activations.
- **Rule Simplification AI:** Converts complex neural patterns into human-readable rules.
- **Rule Consistency Verification:** Ensures logical consistency across extracted rules.
    

5.1.4 Concept Bottleneck System

- **Concept Embedding Space:** Represents predefined interpretable concepts.
- **Bottleneck Constraint Enforcement:** Uses L1 regularization and information bottleneck principles.
- **Concept Activation Vectors:** Implements techniques like TCAV.
- **Sparse Concept Activation:** Ensures only relevant concepts are activated.
- **Concept Hierarchy System:** Organizes concepts into taxonomies.
- **Concept Coverage Analysis:** Evaluates if predefined concepts adequately capture model knowledge.
    

5.1.5 Explanatory Output Generation System

- **Natural Language Explanation Generation:** Trained to produce human-readable explanations.
- **Explanation Faithfulness Verification:** Ensures explanations accurately reflect model reasoning.
- **Multi-Task Learning Framework:** Balances prediction and explanation quality.
- **Reinforcement Learning Component:** Optimizes explanations based on quality/faithfulness.
- **Attention Visualization System:** Highlights influential input elements.
- **Explanation Templates**: Customizable frameworks for different types of explanations.
    

5.2 Integration and Evaluation Systems

5.2.1 Cross-System Integration Framework

- **Unified Training Pipeline:** Jointly trains the LLM and interpretability components.
- **Communication Interfaces:** Facilitates data exchange between the model and auxiliary systems.
- **Resource Allocation AI:** Dynamically balances computational resources.
- **Incremental Update System:** Efficiently updates interpretability structures during training.
    

5.2.2 Evaluation and Validation Systems

- **Fidelity Assessment AI**: To measure how accurately the interpretability structures reflect the model's internal processes.
- **Interpretability Metrics Framework:** Quantifies interpretability across dimensions.
- **Human-AI Collaborative Evaluation:** Tools for human expert assessment.
- **Intervention-based Testing:** Modifies extracted knowledge and measures behavioral effects.
- **Benchmark Testing Framework:** Standardized evaluation across approaches.
    

5.2.3 User Interface and Visualization

- **Multi-modal Visualization System:** Renders graphs, rules, and text.
- **User Adaptation AI:** Customizes presentations based on user expertise.
- **Interactive Exploration Tools:** Navigates and queries interpretability structures.
- **Explainability Dashboards:** Integrated interfaces for different layers.
- **Uncertainty Visualization:** Communicates confidence levels.
    

5.3 Computational Optimization Systems

5.3.1 Efficiency Enhancement

- **Model Parallelism**: Systems for distributing model components across hardware.
- **Pipeline Parallelism**: For efficient sequential processing of different model stages.
- **Data Parallelism**: For processing multiple batches simultaneously.
- **Tensor Parallelism**: For distributing tensor computations.
- **Sparse Activation Sampling:** Selectively processes informative activations.
- **Proxy Model Systems:** Lightweight models approximating full model behavior.
    

5.3.2 Privacy and Security

- **Differential Privacy Implementation**: To prevent leakage of training data through interpretability structures.
- **Adversarial Defense Systems**: To prevent manipulation of interpretability components.
- **Federated Learning Integration**: For privacy-preserving knowledge extraction.
- **Bias Detection AI**: To identify and mitigate biases in extracted knowledge structures.
    

5.4 Research and Development Infrastructure

- **Rapid Prototyping Framework:** For quickly testing different interpretability approaches.
- **Benchmarking Infrastructure:** For comparing performance across different implementations.
- **Cross-disciplinary Collaboration Tools:** To facilitate work between AI researchers, UX specialists, and domain experts.
- **Documentation Generation**: AI systems for maintaining clear documentation of complex interpretability structures.
- **Version Control for Knowledge Structures**: To track evolution of interpretability components during training.
    

5.5 Scaling and Deployment Systems

- **Enterprise Integration Framework**: For deploying interpretable models in production.
- **Regulatory Compliance Systems**: To ensure models meet emerging XAI (Explainable AI) standards.
- **Domain Adaptation Tools**: For customizing interpretability approaches to specific industries.
- **Continuous Learning Infrastructure**: For updating interpretability structures as models evolve.
- **Model Distillation Systems**: For creating smaller, more efficient interpretable models.
    

Pre-Print, Written with AI, 3-10-2025