Say more with less: Understanding prompt learning behaviors through gist compression

[PDF] arxiv.org

Richrag: Crafting rich responses for multi-faceted queries in retrieval-augmented generation

S Wang, X Yu, M Wang, W Chen, Y Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
Retrieval-augmented generation (RAG) effectively addresses issues of static knowledge and
hallucination in large language models. Existing studies mostly focus on question scenarios …
被引用次数：7 相关文章
[PDF] arxiv.org

Domainrag: A chinese benchmark for evaluating domain-specific retrieval-augmented generation

S Wang, J Liu, S Song, J Cheng, Y Fu, P Guo… - arXiv preprint arXiv …, 2024 - arxiv.org
Retrieval-Augmented Generation (RAG) offers a promising solution to address various
limitations of Large Language Models (LLMs), such as hallucination and difficulties in …
被引用次数：6 相关文章 所有 2 个版本
[PDF] arxiv.org

When Text Embedding Meets Large Language Model: A Comprehensive Survey

Z Nie, Z Feng, M Li, C Zhang, Y Zhang, D Long… - arXiv preprint arXiv …, 2024 - arxiv.org
Text embedding has become a foundational technology in natural language processing
(NLP) during the deep learning era, driving advancements across a wide array of …
相关文章 所有 2 个版本