On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions

• URL: http://arxiv.org/abs/2406.10885v3
• Date: Fri, 22 Aug 2025 01:50:40 GMT
• Title: On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions
• Authors: Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song,
• Abstract summary: This paper proposes a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized.<n>We present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization.
• Score: 62.06913340614293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning. Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamiliar or novel situations. This enhances models' inferential capabilities and supports the effective transfer of knowledge across various domains. Despite its significance, the broad nature of this term has led to inconsistencies in understanding conceptualization across various works, as there exists different types of instances that can be abstracted in a wide variety of ways. There is also a lack of a systematic overview that comprehensively examines existing works on the definition, execution, and application of conceptualization to enhance reasoning tasks. In this paper, we address these gaps by first proposing a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized, in order to clarify the term and define the scope of our work. Then, we present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels. Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.

Related papers

• FaCT: Faithful Concept Traces for Explaining Neural Network Decisions [56.796533084868884]
  Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge.<n>We put emphasis on the faithfulness of concept-based explanations and propose a new model with model-inherent mechanistic concept-explanations.<n>Our concepts are shared across classes and, from any layer, their contribution to the logit and their input-visualization can be faithfully traced.
  arXiv  Detail & Related papers  (2025-10-29T13:35:46Z)
• From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models [66.36007274540113]
  Multimodal Large Language Models (MLLMs) strive to achieve a profound, human-like understanding of and interaction with the physical world.<n>They often exhibit a shallow and incoherent integration when acquiring information (Perception) and conducting reasoning (Cognition)<n>This survey introduces a novel and unified analytical framework: From Perception to Cognition"
  arXiv  Detail & Related papers  (2025-09-29T18:25:40Z)
• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains. This paper presents a broad review of recent advances in element-wise ZSIR. We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Evaluating Readability and Faithfulness of Concept-based Explanations [35.48852504832633]
  Concept-based explanations arise as a promising avenue for explaining high-level patterns learned by Large Language Models. Current methods approach concepts from different perspectives, lacking a unified formalization. This makes evaluating the core measures of concepts, namely faithfulness or readability, challenging.
  arXiv  Detail & Related papers  (2024-04-29T09:20:25Z)
• Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
  We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
  arXiv  Detail & Related papers  (2024-02-23T16:50:07Z)
• Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities [28.65224261733876]
  We look at opportunities and challenges in refining the generalisation and transfer of knowledge. Within the domain of reinforcement learning (RL), the representation of knowledge manifests through various modalities. This taxonomy systematically targets these modalities and frames its discussion based on their inherent properties and alignment with different objectives and mechanisms for transfer.
  arXiv  Detail & Related papers  (2023-12-04T14:55:58Z)
• Towards a General Framework for Continual Learning with Pre-training [55.88910947643436]
  We present a general framework for continual learning of sequentially arrived tasks with the use of pre-training. We decompose its objective into three hierarchical components, including within-task prediction, task-identity inference, and task-adaptive prediction. We propose an innovative approach to explicitly optimize these components with parameter-efficient fine-tuning (PEFT) techniques and representation statistics.
  arXiv  Detail & Related papers  (2023-10-21T02:03:38Z)
• A Unified Concept-Based System for Local, Global, and Misclassification Explanations [13.321794212377949]
  We present a unified concept-based system for unsupervised learning of both local and global concepts. Our primary objective is to uncover the intrinsic concepts underlying each data category by training surrogate explainer networks. Our approach facilitates the explanation of both accurate and erroneous predictions.
  arXiv  Detail & Related papers  (2023-06-06T09:28:37Z)
• Disentangling Domain Ontologies [0.0]
  In turn, we propose Conceptual Disentanglement, a multi-level conceptual modelling strategy. We argue why state-of-the-art development methodologies and approaches are insufficient with respect to our characterization.
  arXiv  Detail & Related papers  (2023-03-21T08:36:14Z)
• Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective [44.968170318777105]
  Deep robotic affordance learning (DRAL) aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective, and draw connections between RL and affordances. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.
  arXiv  Detail & Related papers  (2023-03-09T15:42:01Z)
• Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization [0.76146285961466]
  Concept discovery is important for bridging the gap between non-deep learning experts and model end-users. Current approaches include hand-crafting concept datasets and then converting them to latent space directions. In this study, we offer another two approaches to guide user discovery of meaningful concepts, one based on multiple hypothesis testing, and another on interactive visualization.
  arXiv  Detail & Related papers  (2022-02-09T22:29:48Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches [24.786152654589067]
  We give an overview of concept-based explanations and disentanglement approaches. We show that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.
  arXiv  Detail & Related papers  (2021-04-14T15:06:34Z)
• Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach [89.56158561087209]
  We study summarizing on arbitrary aspects relevant to the document. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
  arXiv  Detail & Related papers  (2020-10-14T03:20:46Z)
• Concept Learners for Few-Shot Learning [76.08585517480807]
  We propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation.
  arXiv  Detail & Related papers  (2020-07-14T22:04:17Z)

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.