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.
Advancing Large Language Models with Enhanced Retrieval-Augmented Generation: Evidence from Biological UAV Swarm Control
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
LLMSTP: Empowering Swarm Task Planning with Large Language Models
Springer Verlag | 2025
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