Détail du document
Identifiant
oai:arXiv.org:2405.19519
Sujet
Computer Science - Computation and... Computer Science - Artificial Inte...Auteur
Das, Sudeshna Ge, Yao Guo, Yuting Rajwal, Swati Hairston, JaMor Powell, Jeanne Walker, Drew Peddireddy, Snigdha Lakamana, Sahithi Bozkurt, Selen Reyna, Matthew Sameni, Reza Xiao, Yunyu Kim, Sangmi Chandler, Rasheeta Hernandez, Natalie Mowery, Danielle Wightman, Rachel Love, Jennifer Spadaro, Anthony Perrone, Jeanmarie Sarker, AbeedCatégorie
Computer Science
Année
2024
Date de référencement
05/06/2024
Mots clés
two-layer generationRésumé
Retrieval augmented generation (RAG) provides the capability to constrain generative model outputs, and mitigate the possibility of hallucination, by providing relevant in-context text.
The number of tokens a generative large language model (LLM) can incorporate as context is finite, thus limiting the volume of knowledge from which to generate an answer.
We propose a two-layer RAG framework for query-focused answer generation and evaluate a proof-of-concept for this framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information.
The evaluations demonstrate the effectiveness of the two-layer framework in resource constrained settings to enable researchers in obtaining near real-time data from users.
Das, Sudeshna,Ge, Yao,Guo, Yuting,Rajwal, Swati,Hairston, JaMor,Powell, Jeanne,Walker, Drew,Peddireddy, Snigdha,Lakamana, Sahithi,Bozkurt, Selen,Reyna, Matthew,Sameni, Reza,Xiao, Yunyu,Kim, Sangmi,Chandler, Rasheeta,Hernandez, Natalie,Mowery, Danielle,Wightman, Rachel,Love, Jennifer,Spadaro, Anthony,Perrone, Jeanmarie,Sarker, Abeed, 2024, Two-layer retrieval augmented generation framework for low-resource medical question-answering: proof of concept using Reddit data
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