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Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval

Paper comparing the performance of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retriev

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Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval

By Jungwon Lee, Seungjun Ahn, Daeho Kim, Dongkyun KimAutomation in Construction
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This paper compares the effectiveness of two approaches to retrieving construction safety management knowledge: retrieval-augmented generation (RAG) and fine-tuning large language models.

The study evaluates their performance in a specific domain, providing insights into their strengths and weaknesses. The results can inform the development of more efficient knowledge retrieval systems.

Abstract

This paper compares the effectiveness of two approaches to retrieving construction safety management knowledge: retrieval-augmented generation (RAG) and fine-tuning large language models. The study evaluates their performance in a specific domain, providing insights into their strengths and weaknesses. The results can inform the development of more efficient knowledge retrieval systems.

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knowledge retrievalconstruction safety managementretrieval-augmented generationfine-tuned language modelsperformance comparisonLarge Language ModelsRetrieval & RAGContent EngineeringAI Agents
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