Ra-dit: Retrieval-augmented dual instruction tuning

[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：585 相关文章 所有 4 个版本
[PDF] arxiv.org

Large language models for information retrieval: A survey

Y Zhu, H Yuan, S Wang, J Liu, W Liu, C Deng… - arXiv preprint arXiv …, 2023 - arxiv.org
As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …
被引用次数：179 相关文章 所有 3 个版本
[PDF] arxiv.org

A survey on rag meeting llms: Towards retrieval-augmented large language models

W Fan, Y Ding, L Ning, S Wang, H Li, D Yin… - Proceedings of the 30th …, 2024 - dl.acm.org
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …
被引用次数：32 相关文章 所有 2 个版本
[PDF] arxiv.org

The What, Why, and How of Context Length Extension Techniques in Large Language Models--A Detailed Survey

S Pawar, SM Tonmoy, SM Zaman, V Jain… - arXiv preprint arXiv …, 2024 - arxiv.org
The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural
Language Processing (NLP), contributing to substantial progress in both text …
被引用次数：7 相关文章 所有 4 个版本
[PDF] arxiv.org

Reliable, adaptable, and attributable language models with retrieval

A Asai, Z Zhong, D Chen, PW Koh… - arXiv preprint arXiv …, 2024 - arxiv.org
Parametric language models (LMs), which are trained on vast amounts of web data, exhibit
remarkable flexibility and capability. However, they still face practical challenges such as …
被引用次数：14 相关文章 所有 3 个版本
[PDF] arxiv.org

Uniir: Training and benchmarking universal multimodal information retrievers

C Wei, Y Chen, H Chen, H Hu, G Zhang, J Fu… - arXiv preprint arXiv …, 2023 - arxiv.org
Existing information retrieval (IR) models often assume a homogeneous format, limiting their
applicability to diverse user needs, such as searching for images with text descriptions …
被引用次数：14 相关文章 所有 3 个版本
[PDF] arxiv.org

Chatqa: Building gpt-4 level conversational qa models

Z Liu, W Ping, R Roy, P Xu, M Shoeybi… - arXiv preprint arXiv …, 2024 - arxiv.org
In this work, we introduce ChatQA, a family of conversational question answering (QA)
models that obtain GPT-4 level accuracies. Specifically, we propose a two-stage instruction …
被引用次数：15 相关文章 所有 2 个版本
[PDF] arxiv.org

Jmlr: Joint medical llm and retrieval training for enhancing reasoning and professional question answering capability

J Wang, Z Yang, Z Yao, H Yu - arXiv preprint arXiv:2402.17887, 2024 - arxiv.org
With the explosive growth of medical data and the rapid development of artificial intelligence
technology, precision medicine has emerged as a key to enhancing the quality and …
被引用次数：8 相关文章 所有 2 个版本
[PDF] arxiv.org

Evaluating copyright takedown methods for language models

B Wei, W Shi, Y Huang, NA Smith, C Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Language models (LMs) derive their capabilities from extensive training on diverse data,
including potentially copyrighted material. These models can memorize and generate …
被引用次数：4 相关文章 所有 3 个版本
[PDF] arxiv.org

Searching for best practices in retrieval-augmented generation

X Wang, Z Wang, X Gao, F Zhang, Y Wu, Z Xu… - arXiv preprint arXiv …, 2024 - arxiv.org
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …
被引用次数：4 相关文章 所有 2 个版本