Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control
As research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may augment human cognitive capabilities, they still...
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MDPI AG
2025-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/361 |
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| author | Jin-Xing Hao Lei Chen Luyao Meng |
| author_facet | Jin-Xing Hao Lei Chen Luyao Meng |
| author_sort | Jin-Xing Hao |
| collection | DOAJ |
| description | As research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may augment human cognitive capabilities, they still face significant hallucination and interpretability issues in domain-specific applications. To address these challenges, this study designs and evaluates a domain-specific LLM for the biological UAV swarm control using an enhanced Retrieval-Augmented Generation (RAG) framework. In particular, this study proposes an element-based chunking strategy to build the domain-specific knowledge base and develops novel hybrid retrieval and reranking modules to improve the classical RAG framework. This study also carefully conducts automatic and expert evaluations of our domain-specific LLM, demonstrating the advantages of our model regarding accuracy, relevance, and human alignment. |
| format | Article |
| id | doaj-art-cfe956f1f1574d25837e52f55e4c5e8a |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-cfe956f1f1574d25837e52f55e4c5e8a2025-08-20T01:56:16ZengMDPI AGDrones2504-446X2025-05-019536110.3390/drones9050361Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm ControlJin-Xing Hao0Lei Chen1Luyao Meng2Department of Information Systems, Beihang University, Beijing 100191, ChinaDepartment of Information Systems, Beihang University, Beijing 100191, ChinaDepartment of Information Systems, Beihang University, Beijing 100191, ChinaAs research on biological unmanned aerial vehicle (UAV) swarm control has blossomed, professionals face increasing time and cognitive pressure in mastering the rapidly growing domain knowledge. Although recent general large language models (LLMs) may augment human cognitive capabilities, they still face significant hallucination and interpretability issues in domain-specific applications. To address these challenges, this study designs and evaluates a domain-specific LLM for the biological UAV swarm control using an enhanced Retrieval-Augmented Generation (RAG) framework. In particular, this study proposes an element-based chunking strategy to build the domain-specific knowledge base and develops novel hybrid retrieval and reranking modules to improve the classical RAG framework. This study also carefully conducts automatic and expert evaluations of our domain-specific LLM, demonstrating the advantages of our model regarding accuracy, relevance, and human alignment.https://www.mdpi.com/2504-446X/9/5/361UAVdomain specificationslarge language modelsretrieval-augmented generation |
| spellingShingle | Jin-Xing Hao Lei Chen Luyao Meng Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control Drones UAV domain specifications large language models retrieval-augmented generation |
| title | Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control |
| title_full | Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control |
| title_fullStr | Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control |
| title_full_unstemmed | Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control |
| title_short | Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control |
| title_sort | advancing large language models with enhanced retrieval augmented generation evidence from biological uav swarm control |
| topic | UAV domain specifications large language models retrieval-augmented generation |
| url | https://www.mdpi.com/2504-446X/9/5/361 |
| work_keys_str_mv | AT jinxinghao advancinglargelanguagemodelswithenhancedretrievalaugmentedgenerationevidencefrombiologicaluavswarmcontrol AT leichen advancinglargelanguagemodelswithenhancedretrievalaugmentedgenerationevidencefrombiologicaluavswarmcontrol AT luyaomeng advancinglargelanguagemodelswithenhancedretrievalaugmentedgenerationevidencefrombiologicaluavswarmcontrol |