Results 81 to 90 of about 14,626 (158)

Feedback Adaptation for Retrieval-Augmented Generation

CoRR
Accepted at ACL 2026 ...
Jihwan Bang, Seunghan Yang, Kyuhong Shim, Simyung Chang, Juntae Lee, Sungha Choi   +5 more
openaire   +2 more sources

Retrieval augmented generation for building datasets from scientific literature

JPhys Materials
In this work, we show that employing retrieval augmented generation (RAG) with a large language model (LLM) enables us to extract accurate data from scientific literature and construct datasets.
Piyush Ranjan Maharana, Ashwini Verma, Kavita Joshi   +2 more
doaj   +1 more source

Benchmarking Retrieval-Augmented Generation for Medicine

Findings of the Association for Computational Linguistics ACL 2024
Homepage: https://teddy-xionggz.github.io/benchmark-medical-rag/
Guangzhi Xiong, Qiao Jin 0001, Zhiyong Lu, Aidong Zhang 0001   +3 more
openaire   +2 more sources

Benchmarking Retrieval-Augmented Generation for Chemistry

CoRR
Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high ...
Xianrui Zhong, Bowen Jin, Siru Ouyang, Yanzhen Shen, Qiao Jin 0001, Yin Fang, Zhiyong Lu, Jiawei Han 0001   +7 more
openaire   +2 more sources

Retrieval-augmented patch generation for geosynchronous satellite status forecasting

Scientific Reports
Accurate geosynchronous satellites status forecasting is essential for improving space situational awareness and supporting downstream tasks such as maneuver detection and intent inference.
Shu-He Tian, Yu-Qiang Fang, Ya-Sheng Zhang   +2 more
doaj   +1 more source

Retrieval-Augmented Generation for AI-Generated Content: A Survey

Data Science and Engineering
Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC).
Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang, Bin Cui   +9 more
doaj   +1 more source

Clinical entity augmented retrieval for clinical information extraction

npj Digital Medicine
Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval.
Ivan Lopez, Akshay Swaminathan, Karthik Vedula, Sanjana Narayanan, Fateme Nateghi Haredasht, Stephen P. Ma, April S. Liang, Steven Tate, Manoj Maddali, Robert Joseph Gallo, Nigam H. Shah, Jonathan H. Chen   +11 more
doaj   +1 more source

OptoChat: a large language model with retrieval augmented generation for optics

JPhys Photonics
Large language models (LLMs) show strong performance in general text generation and knowledge-based question answering (QA). However, a substantial performance gap remains in optics, a knowledge-intensive scientific field.
Xiaoqing Bao, Hairuo Wang, Silin Chen, Yali Zhang, Wenjun Chen, Kangjian Di, Mengcheng Lv, Minghui Zhao, Guohao Wang, Wenzheng Zhao, Ningmu Zou   +10 more
doaj   +1 more source

Retrieval-Augmented Generation in Oncology: Promises, Pitfalls, and Early Applications. [PDF]

AI Precis Oncol
Thaker NG, Liu W, Waddle M, Showalter T, Mastroleo F, Luh J, Levitt C, Ning M, Loaiza-Bonilla A, Hong J.   +9 more
europepmc   +1 more source

An explainable graph retrieval augmented generation framework for personalized nutrition recommendation. [PDF]

Front Artif Intell
Dindukurthi V, Jain D, Tripathi A, Obbineni JM, Kandasamy I.   +4 more
europepmc   +1 more source