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/2406.10885v1
• Date: Sun, 16 Jun 2024 10:32:41 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: Entity- and event-level conceptualization plays a pivotal role in generalizable reasoning. There is currently a lack of a systematic overview that comprehensively examines existing works in the definition, execution, and application of conceptualization. We present the first comprehensive survey of 150+ papers, categorizing various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy.
• Score: 46.63556358247516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity- and event-level conceptualization, as fundamental elements of human cognition, plays a pivotal role in generalizable reasoning. This process involves abstracting specific instances into higher-level concepts and forming abstract knowledge that can be applied in unfamiliar or novel situations, which can enhance models' inferential capabilities and support the effective transfer of knowledge across various domains. Despite its significance, there is currently a lack of a systematic overview that comprehensively examines existing works in the definition, execution, and application of conceptualization to enhance reasoning tasks. In this paper, we address this gap by presenting the first comprehensive survey of 150+ papers, categorizing 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

• 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)
• 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)
• 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)
• 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)

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.