LLM Interpretability and Explainability: From Black Boxes to Transparent AI Systems

Executive Summary

LLM interpretability and explainability have emerged as critical research areas as language models grow in scale and complexity. While these models achieve remarkable performance, they operate as black boxes, making it challenging to understand their decision-making processes—a significant barrier for deployment in high-stakes domains like healthcare, finance, and legal systems.

This research explores the frontier of LLM interpretability, from traditional post-hoc explanation methods (LIME, SHAP) to cutting-edge mechanistic interpretability approaches. Key developments include sparse autoencoders that decompose polysemantic neurons into interpretable features, circuit tracing that reveals computational pathways, and attention visualization techniques that illuminate model behavior.

The field faces fundamental challenges: the superposition problem (neurons encoding multiple concepts), computational costs at scale, hallucinated explanations, and the performance-explainability tradeoff. Despite progress from organizations like Anthropic and emerging tools like TransformerLens, full transparency remains elusive. Understanding these models is not just an academic pursuit—it's essential for safe AI deployment, regulatory compliance, and building trustworthy systems.

The Interpretability Challenge

Why Interpretability Matters

As language models grow in size and complexity, comprehending their internal mechanisms and decision-making processes becomes progressively challenging. These models operate as black boxes, making it challenging for users to comprehend how inputs are transformed into outputs and the factors influencing model decisions.

The inability to effectively interpret these models has debilitated their use in high-stakes applications such as medicine and raised issues related to regulatory pressure, safety, and alignment. Ensuring transparency and interpretability is paramount, particularly in deploying models within sensitive domains like healthcare or finance.

The Scale Problem

The immensity and opaqueness of LLMs, with models containing tens or hundreds of billions of parameters, makes it infeasible for a human to inspect or even comprehend the units of an LLM, and necessitates efficient algorithms for interpretation.

The Superposition Problem

A fundamental challenge in interpretability is superposition—the phenomenon where LLMs learn far more features than they have neurons, forcing each neuron to be "polysemantic" and respond to multiple unrelated concepts, making it difficult to understand what any single neuron is doing.

Superposition refers to encoding representations of multiple features within a single neuron, allowing neurons to combine and represent multiple features, enabling the model to capture intricate information and handle complex tasks. The opposite concept is monosemanticity, where a monosemantic neuron is dedicated to a single and specific concept, forming a one-to-one correlation between neurons and concepts.

Mechanistic Interpretability: Reverse-Engineering Neural Networks

Overview

Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations. Recently, MI has garnered significant attention for interpreting transformer-based language models (LMs), resulting in many novel insights yet introducing new challenges.

The Transformer Circuits Project

The Transformer Circuits project (transformer-circuits.pub) is a major research initiative focused on mechanistic interpretability, with the stated goal that "nobody really knows how [large language models] work internally" and the Interpretability team strives to change that—to understand these models to better plan for a future of safe AI.

Key Research Areas

Induction Heads: Specialized attention heads within transformer models that help maintain and repeat sequences during in-context learning. Studies on QK Circuits examine how transformers allocate attention by analyzing interactions between Query and Key matrices and provide insights into how models prioritize tokens in decision-making tasks.

Circuit Analysis: Anthropic has recently moved from tracking individual features to tracking "circuits," which are groups of features that show the steps in a model's thinking: how concepts emerge from input words, how those concepts interact to form new concepts, and how those work within the model to generate actions.

Tools and Frameworks

TransformerLens is a library designed to dig into models' internals and reverse engineer how they work, trying to make it easy to get into the field even if you don't work at an industry org with real infrastructure.

An ICML 2025 tutorial on Mechanistic Interpretability for Language Models provides comprehensive coverage of MI techniques, organizing research around specific research questions or tasks.

Sparse Autoencoders: Solving the Superposition Problem

What Are Sparse Autoencoders?

Sparse Autoencoders (SAEs) have emerged as a promising mechanistic interpretability method due to their ability to disentangle complex, superimposed features within LLMs into more interpretable components. SAEs decompose LLM intermediate activations to make them more comprehensible, addressing the problem where single neurons can encode multiple concepts simultaneously—the superposition phenomenon.

How SAEs Work

SAEs aim to solve the superposition problem in neural feature representations by mapping the model's activations into a more monosemantic latent space, where individual features are better aligned with specific concepts in the network. Empirical studies indicate that sparse autoencoders can enhance the interpretability of neural networks, exhibiting higher scores on the autointerpretability metric and increased monosemanticity.

Recent Advances (2025-2026)

A comprehensive 2025 survey covers the technical framework of SAEs, including basic architecture, design improvements, training strategies, feature explanation methods, evaluation metrics, and real-world applications. Recent work notes that while SAEs were developed to extract interpretable features from LLMs, they lack temporal dependency modeling, instantaneous relation representation, and theoretical guarantees.

Researchers have introduced an identifiable temporal causal representation learning framework for LLMs' high-dimensional concept space that extends SAE techniques with temporal causal frameworks to discover meaningful concept relationships.

Sparse Crosscoders

Anthropic has introduced Sparse Crosscoders, which extend SAE capabilities by extracting interpretable features across multiple layers simultaneously rather than analyzing each layer in isolation.

Applications

SAEs are being applied to decompose LLM representations and identify directions corresponding to code correctness, helping understand internal correctness mechanisms for safe deployment. Modified SAE architectures that explicitly model semantic hierarchies of concepts have been developed, showing that semantic hierarchy can be learned while improving both reconstruction and interpretability.

Anthropic's Circuit Tracing: Opening the Black Box

Circuit Tracing Methodology

Anthropic's approach generates attribution graphs, which partially reveal the steps a model took internally to decide on a particular output. These attribution graphs are visual representations of the computational path a model takes to generate a specific output, highlighting the most important components and interactions.

Open-Source Release

Anthropic has open-sourced their circuit tracing method so that anyone can build on their research. This release enables researchers to:

Trace circuits on supported models by generating their own attribution graphs
Visualize and annotate graphs in an interactive frontend
Test hypotheses by modifying feature values and observing how model outputs change

Technical Approach

The circuit-tracer library uses transcoders, specifically cross-layer MLP transcoders, which replace the model's raw neurons (often polysemantic and hard to interpret) with more interpretable "features" that represent human-understandable concepts.

Recent Landmark Papers

Anthropic published two landmark papers—"Circuit Tracing: Revealing Computational Graphs in Language Models" and "On the Biology of a Large Language Model"—which introduce a novel empirical methodology inspired by neuroscience to dissect the computational substrates of Claude 3.5 Haiku, providing rigorous evidence for latent model behaviors including multistep planning, cross-linguistic generalization, and domain-specific circuit modularity.

Attention Visualization: Understanding Model Focus

Visualization Tools

BertViz is an interactive tool for visualizing attention in Transformer language models that can be run inside a Jupyter or Colab notebook through a simple Python API supporting most Huggingface models. BertViz visualizes the attention mechanism of transformer models at multiple scales, including model-level, attention head-level, and neuron-level.

Hugging Face Transformers interpret offers tools for model interpretability, including visualization of attention heads and attention scores. TensorFlow allows users to create and visualize attention heatmaps for transformer models, which offer insights into how the model distributes its attention across different parts of the input sequence.

Visualization Techniques

From the original paper on vision transformers (ViT), visualizing attention scores as a heat map (called saliency maps or attention maps) has become an important and routine way to inspect the decision-making process of ViT models.

Attention rollout is a recursive algorithm to combine attention scores across all layers, by computing the dot product of successive attention maps. Grad-CAM (Visual explanations from deep networks via gradient-based localization) is another technique that can be applied to visualize attention in neural networks.

Beyond Simple Attention

A novel way to compute relevancy for Transformer networks assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This approach addresses limitations of simply using attention maps directly.

Visualizing attention weights illuminates one type of architecture within the model but does not necessarily provide a direct explanation for predictions. Methods for visualizing attention weights and interpreting their meaning have been developed to enhance the interpretability of attention-based models.

Traditional Explainability Methods: LIME and SHAP

LIME (Local Interpretable Model-agnostic Explanations)

LIME is a technique that generates local approximations to model predictions. LIME builds local, interpretable models around individual predictions that a black-box model makes by perturbing the single instance being predicted and noting the change in prediction confidence, then fitting a simple surrogate model to these perturbed samples.

SHAP (SHapley Additive exPlanations)

SHAP is an XAI method based on game theory that aims at explaining any model by considering each feature as a player and the model outcome as the payoff. SHAP provides local and global explanations, meaning that it has the ability to explain the role of the features for all instances and for a specific instance.

Key Differences

SHAP considers different combinations to calculate the feature attribution while LIME fits a local surrogate model, and SHAP provides both global and local explanations while LIME is limited to local explanations only. In theory, SHAP is the better approach as it provides mathematical guarantees for the accuracy and consistency of explanations, though in practice, the model agnostic implementation of SHAP is slow, even with approximations.

Application to LLMs

Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients are used for explaining classification in LLMs like Llama. Large language models have been used to detect misinformation and the reasoning behind the classifications has been discussed qualitatively and quantitatively using explainability techniques.

Limitations

Features collinearity and nonlinear dependency across features impact the outcomes of both methods, limiting their reliability, and the Shapley method suffers from the inclusion of unrealistic data instances when features are correlated. Despite the limitations of SHAP and LIME in terms of uncertainty estimates, generalization, nonlinear dependencies, feature dependencies, and inability to infer causality, they hold substantial value for explaining and interpreting complex ML models.

Probing Classifiers: Understanding Learned Representations

What Are Probing Classifiers?

Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is simple—a classifier is trained to predict some linguistic property from a model's representations—and has been used to examine a wide variety of models and properties.

How Probing Works

Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. This additional classifier is trained to predict specific linguistic properties or features, such as part-of-speech tags, syntactic structures, sentiment, or named entities. The probing process involves fine-tuning the classifier while keeping the pre-trained model's parameters fixed.

Benefits

Probing classifiers help shed light on how complex machine learning models represent and process different linguistic aspects. When a model makes a correct prediction on a task it has been trained on, probing classifiers can be used to identify if the model actually contains the relevant information or knowledge required to make that prediction, or if it is just making a lucky guess.

Limitations

Correlation vs. Causation: Probing often indicates correlations, not causal relationships, between representations and properties.

Probe Complexity: The probing performance may tell us more about the probe than about the model. The probe may memorize information, rather than evaluate information found in representations.

Best Practices

Control tasks have been designed, which a probe may only solve by memorizing. In particular, researchers randomize the labels in the dataset, creating a new dataset. Probing work should report the possible trade-offs between accuracy and complexity, along a range of probes, and call for using probes that are both simple and accurate.

Neuron Activation Patterns and Feature Visualization

Visualization Systems

NeuronautLLM introduces a visual analysis system for identifying and visualizing influential neurons in transformer-based language models as they relate to user-defined prompts. Neuron to Graph (N2G) is an innovative tool that automatically extracts a neuron's behaviour from the dataset it was trained on and translates it into an interpretable graph.

Understanding Activation Patterns

Correct reasoning is supported by structured activation patterns formed by a small subset of neurons, rather than uniformly distributed across entire layers. Recent research shows that neurons whose average activation polarity differs between correct and incorrect reasoning trajectories are particularly discriminative.

There was a fascinating progression of neuron activation patterns throughout the layers of GPT-J-6B. In the initial layers, sparse and mixed token activations were observed, a phenomenon indicative of early-stage processing where input features are still being deciphered.

Feature Visualization Techniques

At its core, it works by evolving a particular input (say, a picture) from random noise that maximizes a particular network part's activation (such as a neuron). GPT-4 was shown examples of contexts where a given neuron was active and was tasked to provide a short explanation that could capture the activation patterns.

Important Neuron Types

Prior interpretability works have studied neurons to understand the inner mechanism of LLMs and have led to the discovery of many interesting types of neurons such as knowledge neurons, skill neurons, sentiment neurons, concept neurons, and universal neurons.

Challenges

Interpreting LLMs and visualizing their components is extremely difficult due to the incredible scale and high dimensionality of model data. Neuron graphs capture neuron behaviour well for early layers of the model but only partially capture the behaviour for later layers due to increasingly complex neuron behaviour.

Production Deployment Challenges

1. Complexity and Opacity

2. Hallucinated Explanations

New capabilities raise new challenges, such as hallucinated explanations and immense computational costs. Flexible explanations provided in natural language can quickly become less grounded in evidence, and hallucinated explanations are unhelpful or even misleading.

3. High-Stakes Application Requirements

4. Performance vs. Explainability Trade-off

There is inherent tension between model performance and explainability, where simpler, more transparent models often struggle to match the sophisticated capabilities of complex LLMs.

5. Production-Specific Challenges

Enterprises face challenges related to size, latency, bias, fairness, and generated result quality in production deployment. Additional concerns include memory limitations, scalability issues, and network latency that can impact user experience.

The Future of LLM Interpretability

Current State of Research

An approach to studying the internal workings of a model known as mechanistic interpretability has emerged, with Anthropic inventing a way to make large language models easier to understand by building a special second model using sparse autoencoders that works in a more transparent way than normal LLMs. However, there's been excitement over the last couple of years about the possibility of fully explaining how these models work, though that excitement has ebbed, as some researchers feel "it doesn't really feel like it's going anywhere."

Context-Based Approaches

A novel approach to interpretability has been proposed by shifting the focus to understanding the model's functionality within specific contexts through interaction techniques, exploring how contextual information and interaction techniques can elucidate the model's thought processes.

Academic and Industry Engagement

EXPLAINABILITY 2026 - The Third International Conference on Systems Explainability is being held, offering both onsite and online participation options. The conference covers topics including explainable AI for Large Language Models, LIME, SHAP, and interpretability methods.

Why This Matters

As AI finds broad and practical applications in healthcare, law, education, and finance, understanding the mechanisms behind model decisions becomes increasingly critical, with mechanistic interpretability offering a way to remove ambiguity from AI systems and ensure that models are robust, transparent, reliable, and aligned with human values.

The internal mechanisms of LLMs are still unclear and this lack of transparency poses unwanted risks for downstream applications, making understanding and explaining these models crucial for elucidating their behaviors, limitations, and social impacts.

Key Takeaways

Interpretability is Critical: Understanding LLM decision-making is essential for safe deployment in high-stakes domains and regulatory compliance.
The Superposition Problem: The fundamental challenge that neurons encode multiple concepts simultaneously requires sophisticated decomposition techniques.
Mechanistic Interpretability: Reverse-engineering neural networks through circuit tracing and sparse autoencoders shows promise but remains incomplete.
Multiple Approaches Needed: From attention visualization to probing classifiers to LIME/SHAP, different techniques reveal different aspects of model behavior.
Production Challenges: Hallucinated explanations, computational costs, and the performance-explainability tradeoff remain significant barriers.
Open-Source Tools: TransformerLens, BertViz, and Anthropic's circuit tracing enable wider research participation.
Progress but Not Solved: While significant advances have been made, fully transparent and interpretable LLMs remain an aspirational goal.

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