# Defining and Extracting generalizable interaction primitives from DNNs

arXiv: https://arxiv.org/abs/2401.16318 · HTML: https://mlsanity.com/paper/2401.16318/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Rosetta Neurons: Mining the Common Units in a Model Zoo](https://mlsanity.com/paper/2306.09346/) (2023)
- [Discovering and Explaining the Representation Bottleneck of DNNs](https://mlsanity.com/paper/2111.06236/) (2021)
- [Defining and Quantifying the Emergence of Sparse Concepts in DNNs](https://mlsanity.com/paper/2111.06206/) (2021)
- [Does a Neural Network Really Encode Symbolic Concepts?](https://mlsanity.com/paper/2302.13080/) (2023)
- [Explaining Generalization Power of a DNN Using Interactive Concepts](https://mlsanity.com/paper/2302.13091/) (2023)
- [Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability](https://mlsanity.com/paper/2207.11694/) (2022)
- [Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs](https://mlsanity.com/paper/2304.13312/) (2023)
- [Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions](https://mlsanity.com/paper/2303.01506/) (2023)
- [Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?](https://mlsanity.com/paper/2304.01083/) (2023)
- [Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?](https://mlsanity.com/paper/2105.10719/) (2021)
- [Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models](https://mlsanity.com/paper/2305.01939/) (2023)
- [Explaining How a Neural Network Play the Go Game and Let People Learn](https://mlsanity.com/paper/2310.09838/) (2023)

## Built on by (most cited first, 14 of 14)

- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [Towards the Dynamics of a DNN Learning Symbolic Interactions](https://mlsanity.com/paper/2407.19198/) (2024)
- [Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features](https://mlsanity.com/paper/2405.10262/) (2024)
- [Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs](https://mlsanity.com/paper/2405.11880/) (2024)
- [Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions](https://mlsanity.com/paper/2502.10162/) (2025)
- [Technical Report: Quantifying and Analyzing the Generalization Power of a DNN](https://mlsanity.com/paper/2505.06993/) (2025)
- [Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment](https://mlsanity.com/paper/2410.09083/) (2024)
- [Cross-LLM Consistency in Inference: Evidence from Shared Interactions](https://mlsanity.com/paper/2606.08129/) (2026)
- [Disentangling Regional Primitives for Image Generation](https://mlsanity.com/paper/2410.04421/) (2024)
- [Towards provable probabilistic safety for scalable embodied AI systems](https://mlsanity.com/paper/2506.05171/) (2025)
- [BACH-V: Bridging Abstract and Concrete Human-Values in Large Language Models](https://mlsanity.com/paper/2601.14007/) (2026)
- [Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective](https://mlsanity.com/paper/2605.17967/) (2026)
- [A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions](https://mlsanity.com/paper/2607.08776/) (2026)
- [Evaluating and Explaining Prompt Sensitivity of LLMs Using Interactions](https://mlsanity.com/paper/2608.18539/) (2026)

## Abstract (arXiv)

Faithfully summarizing the knowledge encoded by a deep neural network (DNN) into a few symbolic primitive patterns without losing much information represents a core challenge in explainable AI. To this end, Ren et al. (2024) have derived a series of theorems to prove that the inference score of a DNN can be explained as a small set of interactions between input variables. However, the lack of generalization power makes it still hard to consider such interactions as faithful primitive patterns encoded by the DNN. Therefore, given different DNNs trained for the same task, we develop a new method to extract interactions that are shared by these DNNs. Experiments show that the extracted interactions can better reflect common knowledge shared by different DNNs.