A Study on the Implementation of Generative AI Services Using an
Enterprise Data-Based LLM Application Architecture
- URL: http://arxiv.org/abs/2309.01105v2
- Date: Mon, 18 Sep 2023 11:36:50 GMT
- Title: A Study on the Implementation of Generative AI Services Using an
Enterprise Data-Based LLM Application Architecture
- Authors: Cheonsu Jeong
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This study presents a method for implementing generative AI services by
utilizing the Large Language Models (LLM) application architecture. With recent
advancements in generative AI technology, LLMs have gained prominence across
various domains. In this context, the research addresses the challenge of
information scarcity and proposes specific remedies by harnessing LLM
capabilities. The investigation delves into strategies for mitigating the issue
of inadequate data, offering tailored solutions. The study delves into the
efficacy of employing fine-tuning techniques and direct document integration to
alleviate data insufficiency. A significant contribution of this work is the
development of a Retrieval-Augmented Generation (RAG) model, which tackles the
aforementioned challenges. The RAG model is carefully designed to enhance
information storage and retrieval processes, ensuring improved content
generation. The research elucidates the key phases of the information storage
and retrieval methodology underpinned by the RAG model. A comprehensive
analysis of these steps is undertaken, emphasizing their significance in
addressing the scarcity of data. The study highlights the efficacy of the
proposed method, showcasing its applicability through illustrative instances.
By implementing the RAG model for information storage and retrieval, the
research not only contributes to a deeper comprehension of generative AI
technology but also facilitates its practical usability within enterprises
utilizing LLMs. This work holds substantial value in advancing the field of
generative AI, offering insights into enhancing data-driven content generation
and fostering active utilization of LLM-based services within corporate
settings.
Related papers
- A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation.
Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge.
RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
arXiv Detail & Related papers (2024-05-10T02:48:45Z) - A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements.
This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation.
It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
arXiv Detail & Related papers (2024-04-17T01:27:42Z) - 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) - How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback.
Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities.
We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
arXiv Detail & Related papers (2024-02-25T20:07:13Z) - LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named
Entity Recognition [67.96794382040547]
$LLM-DA$ is a novel data augmentation technique based on large language models (LLMs) for the few-shot NER task.
Our approach involves employing 14 contextual rewriting strategies, designing entity replacements of the same type, and incorporating noise injection to enhance robustness.
arXiv Detail & Related papers (2024-02-22T14:19:56Z) - A Reliable Knowledge Processing Framework for Combustion Science using
Foundation Models [0.0]
The study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature.
The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy.
The framework consistently delivers accurate domain-specific responses with minimal human oversight.
arXiv Detail & Related papers (2023-12-31T17:15:25Z) - Towards Efficient Generative Large Language Model Serving: A Survey from
Algorithms to Systems [14.355768064425598]
generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data.
However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency.
This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective.
arXiv Detail & Related papers (2023-12-23T11:57:53Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
We make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications.
We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.
arXiv Detail & Related papers (2023-08-21T15:35:16Z) - A Survey on Model Compression for Large Language Models [23.354025348567077]
Large Language Models (LLMs) have revolutionized natural language processing tasks with remarkable success.
Their formidable size and computational demands present significant challenges for practical deployment.
The field of model compression has emerged as a pivotal research area to alleviate these limitations.
arXiv Detail & Related papers (2023-08-15T08:31:05Z)
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.