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[2602.17856] Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
Summary
This paper evaluates the enhancement of scientific literature chatbots using retrieval-augmented generation (RAG), comparing vector and graph-based systems for improved information retrieval.
Why It Matters
The study addresses the growing need for efficient access to scientific literature, particularly in decision-making contexts. By assessing the performance of hybrid retrieval systems, it highlights advancements in AI that can facilitate better research outcomes and knowledge dissemination.
Key Takeaways
- Hybrid retrieval-augmented generation systems can improve chatbot performance.
- Vector and graph-based systems offer distinct advantages in accessing scientific literature.
- Performance evaluation reveals strengths and limitations of each retrieval approach.
- Enhanced chatbots support evidence-based decision-making in research.
- The study contributes to the ongoing development of AI in information retrieval.
Computer Science > Information Retrieval arXiv:2602.17856 (cs) [Submitted on 19 Feb 2026] Title:Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems Authors:Hamideh Ghanadian, Amin Kamali, Mohammad Hossein Tekieh View a PDF of the paper titled Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems, by Hamideh Ghanadian and 2 other authors View PDF HTML (experimental) Abstract:This paper investigates the enhancement of scientific literature chatbots through retrieval-augmented generation (RAG), with a focus on evaluating vector- and graph-based retrieval systems. The proposed chatbot leverages both structured (graph) and unstructured (vector) databases to access scientific articles and gray literature, enabling efficient triage of sources according to research objectives. To systematically assess performance, we examine two use-case scenarios: retrieval from a single uploaded document and retrieval from a large-scale corpus. Benchmark test sets were generated using a GPT model, with selected outputs annotated for evaluation. The comparative analysis emphasizes retrieval accuracy and response relevance, providing insight into the strengths and limitations of each approach. The findings demonstrate the potential of hybrid RAG systems to improve accessibility to scientific knowledge and to support evidence-based d...
