Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM

• URL: http://arxiv.org/abs/2407.18992v1
• Date: Fri, 26 Jul 2024 01:24:52 GMT
• Title: Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM
• Authors: Nianjun Zhou, Dhaval Patel, Shuxin Lin, Fearghal O'Donncha,
• Abstract summary: This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Language Models (LLMs)
• Score: 4.771737213319029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Language Models (LLMs). Our research introduces an automated model-building process, traditionally reliant on intensive collaboration between data scientists and domain experts. We present two primary innovations: a taxonomy-guided prompting generation that facilitates the automatic creation of AI solution recipes and a set of LLM pipelines designed to produce a solution recipe containing a set of artifacts composed of documents, sample data, and models for IAM. These pipelines, guided by standardized principles, enable the generation of initial solution templates for heterogeneous asset classes without direct human input, reducing reliance on extensive domain knowledge and enhancing automation. We evaluate our methodology by assessing asset health and sustainability across a spectrum of ten asset classes. Our findings illustrate the potential of LLMs and taxonomy-based LLM prompting pipelines in transforming asset management, offering a blueprint for subsequent research and development initiatives to be integrated into a rapid client solution.

Related papers

• From Deep Learning to LLMs: A survey of AI in Quantitative Investment [16.199923554636317]
  Quantitative investment (quant) is an emerging, technology-driven approach in asset management. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation.
  arXiv  Detail & Related papers  (2025-03-27T12:10:15Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Sustainable Digitalization of Business with Multi-Agent RAG and LLM [1.6385815610837167]
  This research aims to explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) We propose a sustainable business solution using pre-existing LLMs that can work with diverse datasets.
  arXiv  Detail & Related papers  (2025-01-06T08:14:23Z)
• From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research [13.818244562506138]
  Large Language Models (LLMs) provide a cost-effective and efficient alternative to human annotation. This paper introduces the SILICON" (Systematic Inference with LLMs for Information Classification and Notation) workflow. The workflow integrates established principles of human annotation with systematic prompt optimization and model selection.
  arXiv  Detail & Related papers  (2024-12-19T02:21:41Z)
• An Evaluation-Driven Approach to Designing LLM Agents: Process and Architecture [25.752294816136807]
  Large Language Models (LLMs) have enabled the development of LLM agents capable of autonomously achieving under-specified goals. Conventional approaches, such as pre-defined test cases and code/model redevelopment pipelines, are inadequate for addressing the unique challenges of LLM agent development. This paper introduces an evaluation-driven design approach, inspired by test-driven development, to address these challenges.
  arXiv  Detail & Related papers  (2024-11-21T00:34:30Z)
• Experiences from Using LLMs for Repository Mining Studies in Empirical Software Engineering [12.504438766461027]
  Large Language Models (LLMs) have transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Our research packages a framework, coined Prompt Refinement and Insights for Mining Empirical Software repositories (PRIMES) Our findings indicate that standardizing prompt engineering and using PRIMES can enhance the reliability and accuracy of studies utilizing LLMs.
  arXiv  Detail & Related papers  (2024-11-15T06:08:57Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)
• Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems [57.41621687431203]
  Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. This paper presents a comprehensive vision on how to design universal foundation models tailored towards the deployment of artificial intelligence (AI)-native networks.
  arXiv  Detail & Related papers  (2024-01-30T00:21:41Z)
• A Study on Training and Developing Large Language Models for Behavior Tree Generation [22.632022793663516]
  This paper presents an innovative exploration of the application potential of large language models (LLM) The core contribution of this paper lies in the design of a BT generation framework based on LLM. In order to ensure the effectiveness and executability of the generated BTs, we emphasize the importance of data verification.
  arXiv  Detail & Related papers  (2024-01-16T03:28:29Z)
• SALMON: Self-Alignment with Instructable Reward Models [80.83323636730341]
  This paper presents a novel approach, namely SALMON, to align base language models with minimal human supervision. We develop an AI assistant named Dromedary-2 with only 6 exemplars for in-context learning and 31 human-defined principles.
  arXiv  Detail & Related papers  (2023-10-09T17:56:53Z)
• A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture [0.0]
  This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. The research delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model.
  arXiv  Detail & Related papers  (2023-09-03T07:03:17Z)
• A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
  Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective. We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
  arXiv  Detail & Related papers  (2023-08-22T13:30:37Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)
• Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes [54.13559879916708]
  EVAPORATE is a prototype system powered by large language models (LLMs) Code synthesis is cheap, but far less accurate than directly processing each document with the LLM. We propose an extended code implementation, EVAPORATE-CODE+, which achieves better quality than direct extraction.
  arXiv  Detail & Related papers  (2023-04-19T06:00:26Z)
• Information Extraction in Low-Resource Scenarios: Survey and Perspective [56.5556523013924]
  Information Extraction seeks to derive structured information from unstructured texts. This paper presents a review of neural approaches to low-resource IE from emphtraditional and emphLLM-based perspectives.
  arXiv  Detail & Related papers  (2022-02-16T13:44:00Z)

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.