SARA: Stochastic Adaption of Language Models to In-Domain RAG

In the current landscape of pre-trained large language model (LLM) applications, various methods such as data augmentation, Residual Learning (RL), Curriculum Learning (CL), Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation (RAG), are commonly employed to integrate more targeted informat

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.4108/eai.21-11-2024.2354624
Primary Topic
Topic Modeling
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article
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article

SARA: Stochastic Adaption of Language Models to In-Domain RAG

Peikang Xu, Dan Peng, Xinyu Liu, Ning Chen
Topic Modeling
article

SARA: Stochastic Adaption of Language Models to In-Domain RAG

Peikang Xu, Dan Peng, Xinyu Liu, Ning Chen
article en

Abstract

In the current landscape of pre-trained large language model (LLM) applications, various methods such as data augmentation, Residual Learning (RL), Curriculum Learning (CL), Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation (RAG), are commonly employed to integrate more targeted informat

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SARA: Stochastic Adaption of Language Models to In-Domain RAG — Peikang Xu, Dan Peng, et al. · (2025) | TGRS Research Map | TGRS