Large language models for cyber security: A systematic literature review

HX Xu, SA Wang, N Li, K Wang, Y Zhao, K Chen… - arxiv preprint arxiv …, 2024 - arxiv.org
The rapid advancement of Large Language Models (LLMs) has opened up new
opportunities for leveraging artificial intelligence in various domains, including cybersecurity …

Domain specialization as the key to make large language models disruptive: A comprehensive survey

C Ling, X Zhao, J Lu, C Deng, C Zheng, J Wang… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

A survey on model compression for large language models

X Zhu, J Li, Y Liu, C Ma, W Wang - Transactions of the Association for …, 2024 - direct.mit.edu
Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Metamath: Bootstrap your own mathematical questions for large language models

L Yu, W Jiang, H Shi, J Yu, Z Liu, Y Zhang… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

The false promise of imitating proprietary llms

A Gudibande, E Wallace, C Snell, X Geng, H Liu… - arxiv preprint arxiv …, 2023 - arxiv.org
An emerging method to cheaply improve a weaker language model is to finetune it on
outputs from a stronger model, such as a proprietary system like ChatGPT (eg, Alpaca, Self …

Efficient large language models: A survey

Z Wan, X Wang, C Liu, S Alam, Y Zheng, J Liu… - arxiv preprint arxiv …, 2023 - arxiv.org
Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Large language models as commonsense knowledge for large-scale task planning

Z Zhao, WS Lee, D Hsu - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

Promptbreeder: Self-referential self-improvement via prompt evolution

C Fernando, D Banarse, H Michalewski… - arxiv preprint arxiv …, 2023 - arxiv.org
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the
reasoning abilities of Large Language Models (LLMs) in various domains. However, such …

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 …

Query rewriting for retrieval-augmented large language models

X Ma, Y Gong, P He, H Zhao, N Duan - arxiv preprint arxiv:2305.14283, 2023 - arxiv.org
Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read
pipeline, making remarkable progress in knowledge-intensive tasks. This work introduces a …