Deploying Large Language Models With Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2411.11895v1
• Date: Thu, 07 Nov 2024 22:11:51 GMT
• Title: Deploying Large Language Models With Retrieval Augmented Generation
• Authors: Sonal Prabhune, Donald J. Berndt,
• Abstract summary: Retrieval Augmented Generation has emerged as a key approach for integrating knowledge from data sources outside of the large language model's training set. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval.
• Score: 0.21485350418225244
• License:
• Abstract: Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data. Retrieval Augmented Generation (RAG) has emerged as a key approach for integrating knowledge from data sources outside of the LLM's training set, including proprietary and up-to-date information. While many research papers explore various RAG strategies, their true efficacy is tested in real-world applications with actual data. The journey from conceiving an idea to actualizing it in the real world is a lengthy process. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval. Additionally, we examine the impacts on the information value chain, encompassing people, processes, and technology. Our aim is to identify the opportunities and challenges of implementing this emerging technology, particularly within the context of behavioral research in the information systems (IS) field. The contributions of this work include the development of best practices and recommendations for adopting this promising technology while ensuring compliance with industry regulations through a proposed AI governance model.

Related papers

• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions [0.0]
  RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Future research directions are proposed, focusing on improving the robustness of RAG models.
  arXiv  Detail & Related papers  (2024-10-03T22:29:47Z)
• Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
  Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs) We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
  arXiv  Detail & Related papers  (2024-09-16T09:06:44Z)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• 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 Closer Look at the Limitations of Instruction Tuning [52.587607091917214]
  We show that Instruction Tuning (IT) fails to enhance knowledge or skills in large language models (LLMs) We also show that popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets.
  arXiv  Detail & Related papers  (2024-02-03T04:45:25Z)
• FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training? [3.0406004578714008]
  The rapid evolution of Large Language Models highlights the necessity for ethical considerations and data integrity in AI development. While FAIR principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area. We propose a novel framework designed to integrate FAIR principles into the LLM development lifecycle.
  arXiv  Detail & Related papers  (2024-01-19T21:21:02Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• Retrieval-Augmented Generation for Large Language Models: A Survey [17.82361213043507]
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
  arXiv  Detail & Related papers  (2023-12-18T07:47:33Z)
• 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)
• 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)

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.