Prompt-guided retrieval augmentation for non-knowledge-intensive tasks

[PDF] arxiv.org

Retrieval-augmented generation for large language models: A survey

Y Gao, Y Xiong, X Gao, K Jia, J Pan, Y Bi, Y Dai… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …
被引用次数：645 相关文章 所有 4 个版本
[PDF] arxiv.org

Personal llm agents: Insights and survey about the capability, efficiency and security

Y Li, H Wen, W Wang, X Li, Y Yuan, G Liu, J Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …
被引用次数：65 相关文章 所有 3 个版本
[PDF] arxiv.org

Retrieval-Augmented Generation for Natural Language Processing: A Survey

S Wu, Y Xiong, Y Cui, H Wu, C Chen, Y Yuan… - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …
被引用次数：4 相关文章 所有 3 个版本
[PDF] arxiv.org

Self-knowledge guided retrieval augmentation for large language models

Y Wang, P Li, M Sun, Y Liu - arXiv preprint arXiv:2310.05002, 2023 - arxiv.org
Large language models (LLMs) have shown superior performance without task-specific fine-
tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be …
被引用次数：48 相关文章 所有 5 个版本
[PDF] arxiv.org

Improving natural language understanding with computation-efficient retrieval representation fusion

S Wu, Y Xiong, Y Cui, X Liu, B Tang, TW Kuo… - arXiv preprint arXiv …, 2024 - arxiv.org
Retrieval-based augmentations that aim to incorporate knowledge from an external
database into language models have achieved great success in various knowledge …
被引用次数：2 相关文章 所有 3 个版本
[PDF] arxiv.org

BioRAG: A RAG-LLM Framework for Biological Question Reasoning

C Wang, Q Long, X Meng, X Cai, C Wu, Z Meng… - arXiv preprint arXiv …, 2024 - arxiv.org
The question-answering system for Life science research, which is characterized by the
rapid pace of discovery, evolving insights, and complex interactions among knowledge …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning

X Liang, S Niu, S Zhang, S Song, H Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-
time knowledge into large language models (LLMs). Nevertheless, constructing and …
相关文章 所有 2 个版本
[PDF] arxiv.org

Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study

Z Nguyen, A Annunziata, V Luong, S Dinh, Q Le… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper investigates the impact of domain-specific model fine-tuning and of reasoning
mechanisms on the performance of question-answering (Q&A) systems powered by large …
被引用次数：2 相关文章 所有 2 个版本
[PDF] aclanthology.org

Aggregating Impressions on Celebrities and their Reasons from Microblog Posts and Web Search Pages

H Yokoyama, R Tsuchida, K Buma… - Proceedings of the …, 2024 - aclanthology.org
This paper aims to augment fans' ability to critique and exploreinformation related to
celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters …
相关文章 所有 3 个版本