{"total":29,"rows":[{"id":"2402.13178","title":"Benchmarking Retrieval-Augmented Generation for Medicine","created":"2024-02-20","cited_by_count":331,"area":"rag"},{"id":"2312.07559","title":"PaperQA: Retrieval-Augmented Generative Agent for Scientific Research","created":"2023-12-08","cited_by_count":149,"area":"rag"},{"id":"2303.01229","title":"Almanac: Retrieval-Augmented Language Models for Clinical Medicine","created":"2023-03-01","cited_by_count":123,"area":"rag"},{"id":"2408.04187","title":"Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation","created":"2024-08-08","cited_by_count":90,"area":"rag"},{"id":"2303.00534","title":"RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training","created":"2023-03-01","cited_by_count":31,"area":"rag"},{"id":"2502.09304","title":"KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG","created":"2025-02-13","cited_by_count":29,"area":"rag"},{"id":"2402.12352","title":"Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge","created":"2024-02-19","cited_by_count":20,"area":"rag"},{"id":"2504.10499","title":"Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey","created":"2025-04-08","cited_by_count":18,"area":"rag"},{"id":"2305.17116","title":"Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model","created":"2023-05-26","cited_by_count":15,"area":"rag"},{"id":"2505.04846","title":"HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights","created":"2025-05-07","cited_by_count":11,"area":"rag"},{"id":"2501.14998","title":"Federated Retrieval Augmented Generation for Multi-Product Question Answering","created":"2025-01-25","cited_by_count":10,"area":"rag"},{"id":"2506.17277","title":"Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation","created":"2025-06-13","cited_by_count":6,"area":"rag"},{"id":"2210.06345","title":"Variational Open-Domain Question Answering","created":"2022-09-23","cited_by_count":5,"area":"rag"},{"id":"2508.01643","title":"ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings","created":"2025-08-03","cited_by_count":5,"area":"rag"},{"id":"2511.01386","title":"RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets","created":"2025-11-03","cited_by_count":5,"area":"rag"},{"id":"2409.15815","title":"AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support","created":"2024-09-24","cited_by_count":3,"area":"rag"},{"id":"2510.10828","title":"VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering","created":"2025-10-12","cited_by_count":3,"area":"rag"},{"id":"2411.16523","title":"LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation","created":"2024-11-25","cited_by_count":2,"area":"rag"},{"id":"2509.10467","title":"DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph","created":"2025-08-22","cited_by_count":2,"area":"rag"},{"id":"2501.05155","title":"Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier","created":"2025-01-09","cited_by_count":1,"area":"rag"},{"id":"2509.19980","title":"RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis","created":"2025-09-24","cited_by_count":1,"area":"rag"},{"id":"2510.04757","title":"ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever","created":"2025-10-06","cited_by_count":1,"area":"rag"},{"id":"2602.00586","title":"RAG-GNN: Integrating Retrieved Knowledge with Graph Neural Networks for Precision Medicine","created":"2026-01-31","cited_by_count":1,"area":"rag"},{"id":"2602.03368","title":"Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain","created":"2026-02-03","cited_by_count":1,"area":"rag"},{"id":"2510.18339","title":"ECG-LLM -- training and evaluation of domain-specific large language models for electrocardiography","created":"2025-10-21","cited_by_count":0,"area":"rag"},{"id":"2601.04531","title":"Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering","created":"2026-01-08","cited_by_count":0,"area":"rag"},{"id":"2604.07274","title":"A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering","created":"2026-04-08","cited_by_count":0,"area":"rag"},{"id":"2605.01664","title":"A Hybrid Retrieval and Reranking Framework for Evidence-Grounded Retrieval-Augmented Generation","created":"2026-05-03","cited_by_count":0,"area":"rag"},{"id":"2605.16535","title":"RAPT: Retrieval-Augmented Post-hoc Thresholding for Multi-Label Classification","created":"2026-05-15","cited_by_count":0,"area":"rag"}],"next_cursor":null}