FAME: Introducing Fuzzy Additive Models for Explainable AI

• URL: http://arxiv.org/abs/2504.07011v1
• Date: Wed, 09 Apr 2025 16:29:55 GMT
• Title: FAME: Introducing Fuzzy Additive Models for Explainable AI
• Authors: Omer Bahadir Gokmen, Yusuf Guven, Tufan Kumbasar,
• Abstract summary: We introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI)<n>FAME captures the input-output relationships with fewer active rules, thus improving clarity.<n>We show that FAME captures the input-output relationships with fewer active rules, thus improving clarity.
• Score: 2.526146573337397
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this study, we introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI). The family consists of three layers: (1) a Projection Layer that compresses the input space, (2) a Fuzzy Layer built upon Single Input-Single Output Fuzzy Logic Systems (SFLS), where SFLS functions as subnetworks within an additive index model, and (3) an Aggregation Layer. This architecture integrates the interpretability of SFLS, which uses human-understandable if-then rules, with the explainability of input-output relationships, leveraging the additive model structure. Furthermore, using SFLS inherently addresses issues such as the curse of dimensionality and rule explosion. To further improve interpretability, we propose a method for sculpting antecedent space within FAM, transforming it into FAME. We show that FAME captures the input-output relationships with fewer active rules, thus improving clarity. To learn the FAM family, we present a deep learning framework. Through the presented comparative results, we demonstrate the promising potential of FAME in reducing model complexity while retaining interpretability, positioning it as a valuable tool for XAI.

Related papers

• Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate [0.0]
  This paper explores an alternative, constructive approach to model development, built upon the foundation of non-trainable, deterministic input embeddings.<n>We show that specialist models trained on disparate datasets can be merged into a single, more capable Mixture-of-Experts model.<n>We introduce a layer-wise constructive training methodology, where a deep Transformer is "grown" by progressively stacking and training one layer at a time.
  arXiv  Detail & Related papers  (2025-07-08T20:01:15Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? [76.15163242945813]
  Large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.<n>We introduce a novel generative model that generates tokens on the basis of human-interpretable concepts represented as latent discrete variables.
  arXiv  Detail & Related papers  (2025-03-12T01:21:17Z)
• Disentangling Memory and Reasoning Ability in Large Language Models [97.26827060106581]
  We propose a new inference paradigm that decomposes the complex inference process into two distinct and clear actions. Our experiment results show that this decomposition improves model performance and enhances the interpretability of the inference process.
  arXiv  Detail & Related papers  (2024-11-20T17:55:38Z)
• X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection [55.77552681618732]
  X2-DFD is an eXplainable and eXtendable framework based on multimodal large-language models (MLLMs) for deepfake detection.<n>The first stage, Model Feature Assessment, systematically evaluates the detectability of forgery-related features for the MLLM.<n>The second stage, Explainable dataset Construction, consists of two key modules: Strong Feature Strengthening and Weak Feature Supplementing.<n>The third stage, Fine-tuning and Inference, involves fine-tuning the MLLM on the constructed dataset and deploying it for final detection and explanation.
  arXiv  Detail & Related papers  (2024-10-08T15:28:33Z)
• Distribution Guided Active Feature Acquisition [14.279123976398926]
  We develop an active feature acquisition framework that interacts with the environment to obtain new information on-the-fly. We build our AFA framework on a backbone of understanding the information and conditional dependencies that are present in the data. We show that it is possible to guide the training of RL agents for AFA via side-information and auxiliary rewards stemming from our generative models.
  arXiv  Detail & Related papers  (2024-10-04T20:38:30Z)
• Talking Heads: Understanding Inter-layer Communication in Transformer Language Models [32.2976613483151]
  We analyze a mechanism used in two LMs to selectively inhibit items in a context in one task.<n>We find that models write into low-rank subspaces of the residual stream to represent features which are then read out by later layers.
  arXiv  Detail & Related papers  (2024-06-13T18:12:01Z)
• Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property [8.793424363526212]
  We propose a model-agnostic local explanation method to estimate the two-level feature attributions simultaneously. A key idea of the proposed method is to introduce the consistency property that should exist between the HiFAs and LoFAs. Thanks to this consistency property, the proposed method can produce HiFAs and LoFAs that are both faithful to the black-box models and consistent with each other.
  arXiv  Detail & Related papers  (2024-05-23T13:03:26Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge [63.00793292863]
  ToddlerDiffusion is a novel approach to decomposing the complex task of RGB image generation into simpler, interpretable stages. Our method, termed ToddlerDiffusion, cascades modality-specific models, each responsible for generating an intermediate representation. ToddlerDiffusion consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-11-24T15:20:01Z)
• Towards LLM-guided Causal Explainability for Black-box Text Classifiers [16.36602400590088]
  We aim to leverage the instruction-following and textual understanding capabilities of recent Large Language Models to facilitate causal explainability. We propose a three-step pipeline via which, we use an off-the-shelf LLM to identify the latent or unobserved features in the input text. We experiment with our pipeline on multiple NLP text classification datasets, and present interesting and promising findings.
  arXiv  Detail & Related papers  (2023-09-23T11:22:28Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Interpretability at Scale: Identifying Causal Mechanisms in Alpaca [62.65877150123775]
  We use Boundless DAS to efficiently search for interpretable causal structure in large language models while they follow instructions. Our findings mark a first step toward faithfully understanding the inner-workings of our ever-growing and most widely deployed language models.
  arXiv  Detail & Related papers  (2023-05-15T17:15:40Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)
• Human-interpretable model explainability on high-dimensional data [8.574682463936007]
  We introduce a framework for human-interpretable explainability on high-dimensional data, consisting of two modules. First, we apply a semantically meaningful latent representation, both to reduce the raw dimensionality of the data, and to ensure its human interpretability. Second, we adapt the Shapley paradigm for model-agnostic explainability to operate on these latent features. This leads to interpretable model explanations that are both theoretically controlled and computationally tractable.
  arXiv  Detail & Related papers  (2020-10-14T20:06:28Z)
• PIN: A Novel Parallel Interactive Network for Spoken Language Understanding [68.53121591998483]
  In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
  arXiv  Detail & Related papers  (2020-09-28T15:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.