Results 11 to 20 of about 16,728 (252)

Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a hybrid architecture that combines the generative power of large language models (LLMs) with the factual reliability of information retrieval systems. Although the emergence of large language models (LLMs) has significantly improved the performance of natural language understanding and generation tasks. However,
Jingsong Shawn Yu, Yazhi Yao
  +10 more sources

Active Retrieval Augmented Generation

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augmenting LMs by retrieving information from external knowledge resources is one promising solution.
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu 0033, Jane Dwivedi-Yu, Yiming Yang 0002, Jamie Callan, Graham Neubig   +8 more
openaire   +3 more sources

Context Tuning for Retrieval Augmented Generation

CoRR, 2023
Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools. Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task.
Raviteja Anantha, Tharun Bethi, Danil Vodianik, Srinivas Chappidi   +3 more
openaire   +3 more sources

Retrieval-Augmented Controllable Review Generation [PDF]

Proceedings of the 28th International Conference on Computational Linguistics, 2020
In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating. This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text.
Jihyeok Kim, Seungtaek Choi, Reinald Kim Amplayo, Seung-won Hwang   +3 more
openaire   +1 more source

Distributed Retrieval-Augmented Generation

CoRR
As large language models (LLMs) become increasingly adopted on edge devices, Retrieval-Augmented Generation (RAG) is gaining prominence as a solution to address factual deficiencies and hallucinations by integrating external knowledge. However, centralized RAG architectures face significant challenges in data privacy and scalability.
Chenhao Xu 0003, Longxiang Gao, Yuan Miao, Xi Zheng 0001   +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

Parametric Retrieval Augmented Generation

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinations, outdated knowledge, and domain adaptation. In particular, existing RAG methods append relevant documents retrieved from external corpus or databases to the input of ...
Weihang Su, Yichen Tang 0001, Qingyao Ai, Junxi Yan, Changyue Wang 0001, Hongning Wang, Ziyi Ye, Yujia Zhou 0002, Yiqun Liu 0001   +8 more
openaire   +2 more sources

Structural and biochemical insights into the thermostable esterase Ta0887 from Thermoplasma acidophilum

FEBS Open Bio, EarlyView.
In this study, a novel esterase from the thermoacidophilic archaeon Thermoplasma acidophilum was biochemically and structurally characterized. Our results demonstrate that Ta0887 is a highly thermostable esterase that preferentially hydrolyzes p‐nitrophenyl hexanoate and possesses an α‐helical cap domain that likely contributes to its substrate ...
Alejandro Delgado‐Rey, Miriam Livier Llamas‐García, Gabriela M. Montero‐Morán, Leticia Santos, Samuel Lara‐González   +4 more
wiley   +1 more source

Next Sentence Prediction with BERT as a Dynamic Chunking Mechanism for Retrieval-Augmented Generation Systems

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Retrieval-Augmented Generation systems enhance the generative capabilities of large language models by grounding their responses in external knowledge bases, addressing some of their major limitations and improving their reliability for tasks requiring ...
Alexandre Thurow Bender, Gabriel Almeida Gomes, Ulisses Brisolara Corrêa, Ricardo Matsumura Araujo   +3 more
doaj   +1 more source

Review of Document Q&A Driven by Multimodal Retrieval-Augmented Generation (Invited) [PDF]

Jisuanji gongcheng
Traditional Retrieval-Augmented Generation (RAG) methods predominantly focus on pure-text scenarios. In these scenarios, their retrieval and generation mechanisms encounter difficulties in effectively modeling common visual elements, spatial layouts, and
LI Zeming, WANG Shuliang, SHANG Zihe, SHENG Ming
doaj   +1 more source