An overview of domain-specific foundation model: key technologies, applications and challenges

• URL: http://arxiv.org/abs/2409.04267v1
• Date: Fri, 6 Sep 2024 13:24:22 GMT
• Title: An overview of domain-specific foundation model: key technologies, applications and challenges
• Authors: Haolong Chen, Hanzhi Chen, Zijian Zhao, Kaifeng Han, Guangxu Zhu, Yichen Zhao, Ying Du, Wei Xu, Qingjiang Shi,
• Abstract summary: This article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models.
• Score: 25.146374189455784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models.

Related papers

• Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification [71.08024880298613]
  We study the multi-source Domain Generalization of text classification. We propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
  arXiv  Detail & Related papers  (2024-09-20T07:46:21Z)
• Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities [5.22475289121031]
  Multimodal models are expected to be a critical component to future advances in artificial intelligence. This work provides a fresh perspective on generalist multimodal models via a novel architecture and training configuration specific taxonomy.
  arXiv  Detail & Related papers  (2024-06-08T15:30:46Z)
• Multi-BERT: Leveraging Adapters and Prompt Tuning for Low-Resource Multi-Domain Adaptation [14.211024633768986]
  The rapid expansion of texts' volume and diversity presents formidable challenges in multi-domain settings. Traditional approaches, either employing a unified model for multiple domains or individual models for each domain, frequently pose significant limitations. This paper introduces a novel approach composed of one core model with multiple sets of domain-specific parameters.
  arXiv  Detail & Related papers  (2024-04-02T22:15:48Z)
• A Survey for Foundation Models in Autonomous Driving [10.315409708116865]
  Large language models contribute to planning and simulation in autonomous driving. vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning.
  arXiv  Detail & Related papers  (2024-02-02T02:44:59Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• CHORUS: Foundation Models for Unified Data Discovery and Exploration [6.85448651843431]
  We show that foundation models are highly applicable to the data discovery and data exploration domain. We show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. This suggests a future direction in which disparate data management tasks can be unified under foundation models.
  arXiv  Detail & Related papers  (2023-06-16T03:58:42Z)
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) They provide a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
  arXiv  Detail & Related papers  (2023-05-30T03:00:30Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• A Case for Business Process-Specific Foundation Models [6.25118865553438]
  We argue that business process data representations have unique characteristics that warrant the development of a new class of foundation models. These models should tackle the unique challenges of applying AI to business processes which include data scarcity, multi-modal representations, domain specific terminology, and privacy concerns.
  arXiv  Detail & Related papers  (2022-10-26T14:17:47Z)
• Polynomial Networks in Deep Classifiers [55.90321402256631]
  We cast the study of deep neural networks under a unifying framework. Our framework provides insights on the inductive biases of each model. The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks.
  arXiv  Detail & Related papers  (2021-04-16T06:41:20Z)

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.