A Comparative Evaluation of Standalone LLMs and Retrieval-Augmented Generation Models Using Hypothetical Gemini Systems

Title

A Comparative Evaluation of Standalone LLMs and Retrieval-Augmented Generation Models Using Hypothetical Gemini Systems

Creator

Prakash, G.L.; Rekha, V.

Description

This study assesses the efficacy of two theoretical language models; Gemini Standalone LLM and Gemini RAG (Retrieval Augmented Generation) across diverse natural language inquiries. The assessment centers on three principal metrics: precision, pertinence, and inference duration. The experiment utilizes a controlled simulation to illustrate the benefits and drawbacks of independent language creation versus retrieval augmented generation strategies. The results demonstrate that RAG at trains superior accuracy and relevance by integrating retrieved context, albeit it incurs longer inference durations. This comparative analysis seeks to assist researchers in comprehending the ramifications of including retrieval methods into big language models. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1863 LNNS;pp.26-37

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Gemini API; Large Language Models; Retrieval-Augmented Generation

Coverage

Prakash G.L., Department of Information Science and Engineering, BMS Institute of Technology and Management, VTU, Bengaluru, India; Rekha V., Department of CSE, SoET, Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303219181-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-19182-3_3

https://www.scopus.com/pages/publications/105039012890?origin=resultslist