APMSR: an intelligent QA system for synthetic biology empowered by adaptive prompting and multi-source knowledge retrieval
Year: 2026
Type: article
Abstract: Synthetic biology is a highly specialized discipline characterized by diverse and heterogeneous knowledge sources. Leveraging large language models (LLMs) to achieve accurate, context-aware domain-specific question answering remains a significant challenge, particularly due to persistent issues such as knowledge gaps and hallucinations. To address this, we propose APMSR (Adaptive Prompt and Multi-Source Retrieval), an optimization strategy that integrates adaptive prompt generation based on question features with dynamic multi-source knowledge retrieval, guided by the LinUCB algorithm. This approach balances exploration and exploitation to enhance the relevance and precision of domain-specific information retrieval. We implemented APMSR in a question-answering system tailored to synthetic biology and evaluated its performance on complex, professional-level queries. Experimental results demonstrate that the APMSR-optimized system achieves up to 93% accuracy on multiple-choice and true/false questions. These improvements in accuracy and robustness highlight the potential of combining LLMs with retrieval-augmented strategies for advanced domain-specific question answering.
Source: Scientific Reports
Authors: Jinlin Wang, Zhizheng Cao, Zhiyong Tian, Bin Sun, Li Cheng
Institution: Hubei University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G8683302537
OpenAlex ID: W7128004746