DOLLmC: DevOps for Large Language model Customization

• URL: http://arxiv.org/abs/2405.11581v2
• Date: Tue, 21 May 2024 04:50:12 GMT
• Title: DOLLmC: DevOps for Large Language model Customization
• Authors: Panos Fitsilis, Vyron Damasiotis, Vasileios Kyriatzis, Paraskevi Tsoutsa,
• Abstract summary: This research aims to establish a scalable and efficient framework for LLM customization. We propose a robust framework that enhances continuous learning, seamless deployment, and rigorous version control of LLMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid integration of Large Language Models (LLMs) into various industries presents both revolutionary opportunities and unique challenges. This research aims to establish a scalable and efficient framework for LLM customization, exploring how DevOps practices should be adapted to meet the specific demands of LLM customization. By integrating ontologies, knowledge maps, and prompt engineering into the DevOps pipeline, we propose a robust framework that enhances continuous learning, seamless deployment, and rigorous version control of LLMs. This methodology is demonstrated through the development of a domain-specific chatbot for the agricultural sector, utilizing heterogeneous data to deliver actionable insights. The proposed methodology, so called DOLLmC, not only addresses the immediate challenges of LLM customization but also promotes scalability and operational efficiency. However, the methodology's primary limitation lies in the need for extensive testing, validation, and broader adoption across different domains.

Related papers

• AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment [13.977849745488339]
  AmoebaLLM is a novel framework designed to enable the instant derivation of large language models of arbitrary shapes. AmoebaLLM significantly facilitates rapid deployment tailored to various platforms and applications.
  arXiv  Detail & Related papers  (2024-11-15T22:02:28Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities [0.35998666903987897]
  This report examines the fine-tuning of Large Language Models (LLMs) It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. The report introduces a structured seven-stage pipeline for fine-tuning LLMs.
  arXiv  Detail & Related papers  (2024-08-23T14:48:02Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Large Language Model Agent as a Mechanical Designer [7.136205674624813]
  In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints.
  arXiv  Detail & Related papers  (2024-04-26T16:41:24Z)
• BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models [56.89958793648104]
  Large Language Models (LLMs) are versatile and capable of addressing a diverse range of tasks. Previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. We present a novel framework named BLADE, which enhances Black-box LArge language models with small Domain-spEcific models.
  arXiv  Detail & Related papers  (2024-03-27T08:57:21Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs [49.32067576992511]
  Large language models often fall short of the performance achieved by domain-specific state-of-the-art models. One potential approach to enhance domain-specific capabilities of LLMs involves fine-tuning them using corresponding datasets. We propose Preference Adaptation for Enhancing Domain-specific Abilities of LLMs (PANDA) Our experimental results reveal that PANDA significantly enhances the domain-specific ability of LLMs on text classification and interactive decision tasks.
  arXiv  Detail & Related papers  (2024-02-20T09:02:55Z)
• 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)

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.