Aligning the capabilities of large language models with the context of information retrieval...

[PDF] aclanthology.org

Data augmentation using llms: Data perspectives, learning paradigms and challenges

B Ding, C Qin, R Zhao, T Luo, X Li… - Findings of the …, 2024 - aclanthology.org
In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …
被引用次数：57 相关文章 所有 3 个版本
[PDF] arxiv.org

BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models

H Li, Q Ai, J Chen, Q Dong, Z Wu, Y Liu, C Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of
addressing a diverse range of tasks. However, general LLMs, which are developed on open …
被引用次数：12 相关文章 所有 2 个版本
[PDF] arxiv.org

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion

P Jia, Y Liu, X Zhao, X Li, C Hao, S Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Query expansion is a commonly-used technique in many search systems to better represent
users' information needs with additional query terms. Existing studies for this task usually …
被引用次数：8 相关文章 所有 3 个版本

Improving Generative Information Retrieval Systems Based on User Feedback

Q Ai, Z Dou, M Zhang - Information Access in the Era of Generative AI, 2024 - Springer
In this chapter, we discuss how to improve Generative Information Retrieval (GenIR) systems
based on user feedback. Before describing the approaches, it is necessary to be aware that …
相关文章 所有 3 个版本