Hybrid Retrieval

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Hybrid retrieval studies how to combine complementary search methods, especially keyword-based methods such as BM25 with dense vector search, and sometimes graphs, metadata, or visual signals. Systems commonly merge ranked lists with reciprocal rank fusion, calibrate or weight scores, and use rerankers, query expansion, or adaptive routing to refine results. The approach is used in settings such as question answering, document search, recommendations, and retrieval-augmented generation, where missing evidence or irrelevant context can undermine downstream answers. There is no universally best combination: gains depend on the domain, query type, and benchmark, while fusion and reranking can add latency or reduce precision.

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91 papers9 papers in the last four weeks, down 10% on the four weeks before. 0.1% of all new papers.

Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
Feb 19, 2026Hamideh Ghanadian, Amin Kamali, Mohammad Hossein TekiehRAG Evaluation Graph Retrieval-Augmented Generation
Pest Management Regulatory Agency (PMRA), Health Canada, Ottawa, ON, Canada · Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada · Oral Health Branch (OHB), Health Canada, Ottawa, ON, Canada

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Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems

Hamideh Ghanadian, Amin Kamali, Mohammad Hossein Tekieh

Pest Management Regulatory Agency (PMRA), Health Canada, Ottawa, ON, Canada · Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada · Oral Health Branch (OHB), Health Canada, Ottawa, ON, Canada

RAG Evaluation Graph Retrieval-Augmented Generation Hybrid Retrieval IR Evaluation Scientific QA

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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 decision making.