Results 11 to 20 of about 36,775 (295)

Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems

Applied Sciences
A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses.
Edmund V. Ndimbo, Qin Luo, Gimo C. Fernando, Xu Yang, Bang Wang   +4 more
doaj   +2 more sources

Corrective Retrieval Augmented Generation

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model ...
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, Zhen-Hua Ling   +3 more
openaire   +3 more sources

Accelerating Retrieval-Augmented Generation

Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1
An evolving solution to address hallucination and enhance accuracy in large language models (LLMs) is Retrieval-Augmented Generation (RAG), which involves augmenting LLMs with information retrieved from an external knowledge source, such as the web. This paper profiles several RAG execution pipelines and demystifies the complex interplay between their ...
Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu, Alireza Salemi, Sukhan Lee 0002, Hamed Zamani, Mohammad Alian   +7 more
openaire   +3 more sources

DuetRAG: Collaborative Retrieval-Augmented Generation

CoRR
5 ...
Dian Jiao, Li Cai, Jingsheng Huang, Wenqiao Zhang, Siliang Tang, Yueting Zhuang   +5 more
openaire   +3 more sources

Evaluation of Retrieval-Augmented Generation: A Survey

Retrieval-Augmented Generation (RAG) has recently gained traction in natural language processing. Numerous studies and real-world applications are leveraging its ability to enhance generative models through external information retrieval. Evaluating these RAG systems, however, poses unique challenges due to their hybrid structure and reliance on ...
Hao Yu 0030, Aoran Gan, Kai Zhang 0038, Shiwei Tong, Qi Liu 0003, Zhaofeng Liu   +5 more
openaire   +3 more sources

RACE: Retrieval-Augmented Commit Message Generation

, 2022
<p>The dataset of ``RACE: Retrieval-Augmented Commit Message Generation``</p ...
anonymous
core   +2 more sources

DF-RAG:A Retrieval-augmented Generation Method Based on Query Rewriting and Knowledge Selection [PDF]

Jisuanji kexue
Large language models have demonstrated formidable comprehension abilities in conversational tasks,yet they still face issues such as data timeliness and inefficiency in handling specific knowledge.To address these challenges,Retrieval-augmented ...
ZHANG Haoran, HAO Wenning, JIN Dawei, CHENG Kai, ZHAI Ying
doaj   +1 more source

A Framework for Evaluating the Retrieval Effectiveness of Search Engines [PDF]

, 2012
This chapter presents a theoretical framework for evaluating next generation search engines. We focuson search engines whose results presentation is enriched with additional information and does notmerely present the usual list of “10 blue links”, that ...
Lewandowski, Dirk
core   +1 more source

Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Mathematics
Retrieval-augmented generation (RAG) leverages the strengths of information retrieval and generative models to enhance the handling of real-time and domain-specific knowledge.
Wan Zhang, Jing Zhang
doaj   +1 more source

Chain-of-Retrieval Augmented Generation

CoRR
Accepted by NeurIPS ...
Liang Wang 0046, Haonan Chen 0005, Nan Yang 0002, Xiaolong Huang, Zhicheng Dou, Furu Wei   +5 more
openaire   +2 more sources