Visualizing Rule Sets: Exploration and Validation of a Design Space

• URL: http://arxiv.org/abs/2103.01022v2
• Date: Thu, 4 Mar 2021 14:57:10 GMT
• Title: Visualizing Rule Sets: Exploration and Validation of a Design Space
• Authors: Jun Yuan, Oded Nov, Enrico Bertini
• Abstract summary: Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. To date there has been limited work on exploring visual alternatives for presenting rules. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.
• Score: 21.659381756612866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visual factors we believe have a positive impact on rule readability and understanding. The paper presents an initial design space for visualizing rule sets and a user study exploring their impact. The results show that some design factors have a strong impact on how efficiently readers can process the rules while having minimal impact on accuracy. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.

Related papers

• LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models [87.49676980090555]
  Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. We introduce LogicGame, a novel benchmark designed to evaluate the comprehensive rule understanding, execution, and planning capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-08-28T13:16:41Z)
• Symbolic Working Memory Enhances Language Models for Complex Rule Application [87.34281749422756]
  Large Language Models (LLMs) have shown remarkable reasoning performance but struggle with multi-step deductive reasoning. We propose augmenting LLMs with external working memory and introduce a neurosymbolic framework for rule application. Our framework iteratively performs symbolic rule grounding and LLM-based rule implementation.
  arXiv  Detail & Related papers  (2024-08-24T19:11:54Z)
• DeepVoting: Learning Voting Rules with Tailored Embeddings [13.037431161285971]
  We recast the problem of designing a good voting rule into one of learning probabilistic versions of voting rules. We show that embeddings of preference profiles derived from the social choice literature allows us to learn existing voting rules more efficiently. We also show that rules learned using embeddings can be tweaked to create novel voting rules with improved axiomatic properties.
  arXiv  Detail & Related papers  (2024-08-24T17:15:20Z)
• Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
  Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
  arXiv  Detail & Related papers  (2024-02-18T03:38:51Z)
• ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning [107.61997887260056]
  We propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs.
  arXiv  Detail & Related papers  (2023-09-04T11:38:02Z)
• Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection [13.772240348963303]
  Rule By Example (RBE) is a novel-based contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches.
  arXiv  Detail & Related papers  (2023-07-24T16:55:37Z)
• RulE: Knowledge Graph Reasoning with Rule Embedding [69.31451649090661]
  We propose a principled framework called textbfRulE (stands for Rule Embedding) to leverage logical rules to enhance KG reasoning. RulE learns rule embeddings from existing triplets and first-order rules by jointly representing textbfentities, textbfrelations and textbflogical rules in a unified embedding space. Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.
  arXiv  Detail & Related papers  (2022-10-24T06:47:13Z)
• An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets [21.659381756612866]
  Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.
  arXiv  Detail & Related papers  (2021-09-19T16:33:16Z)
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
  This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks. Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
  arXiv  Detail & Related papers  (2020-10-08T14:47:02Z)
• Towards Learning Instantiated Logical Rules from Knowledge Graphs [20.251630903853016]
  We present GPFL, a probabilistic learner rule optimized to mine instantiated first-order logic rules from knowledge graphs. GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules. We reveal the presence of overfitting rules, their impact on the predictive performance, and the effectiveness of a simple validation method filtering out overfitting rules.
  arXiv  Detail & Related papers  (2020-03-13T00:32:46Z)

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.