A comprehensive overview of large language models

H Naveed, AU Khan, S Qiu, M Saqib, S Anwar… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Security and privacy on generative data in aigc: A survey

T Wang, Y Zhang, S Qi, R Zhao, X Zhihua… - ACM Computing …, 2023 - dl.acm.org
The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in
the evolution of information technology. With AIGC, it can be effortless to generate high …

The rise and potential of large language model based agents: A survey

Z Xi, W Chen, X Guo, W He, Y Ding, B Hong… - arXiv preprint arXiv …, 2023 - arxiv.org
For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …

Llemma: An open language model for mathematics

Z Azerbayev, H Schoelkopf, K Paster… - arXiv preprint arXiv …, 2023 - arxiv.org
We present Llemma, a large language model for mathematics. We continue pretraining
Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing …

Cumulative reasoning with large language models

Y Zhang, J Yang, Y Yuan, ACC Yao - arXiv preprint arXiv:2308.04371, 2023 - arxiv.org
While language models are powerful and versatile, they often fail to address highly complex
problems. This is because solving complex problems requires deliberate thinking, which has …

Mint: Evaluating llms in multi-turn interaction with tools and language feedback

X Wang, Z Wang, J Liu, Y Chen, L Yuan… - arXiv preprint arXiv …, 2023 - arxiv.org
To solve complex tasks, large language models (LLMs) often require multiple rounds of
interactions with the user, sometimes assisted by external tools. However, current evaluation …

Exploring QCD matter in extreme conditions with Machine Learning

K Zhou, L Wang, LG Pang, S Shi - Progress in Particle and Nuclear Physics, 2024 - Elsevier
In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

Kan 2.0: Kolmogorov-arnold networks meet science

Z Liu, P Ma, Y Wang, W Matusik, M Tegmark - arXiv preprint arXiv …, 2024 - arxiv.org
A major challenge of AI+ Science lies in their inherent incompatibility: today's AI is primarily
based on connectionism, while science depends on symbolism. To bridge the two worlds …

Lego-prover: Neural theorem proving with growing libraries

H Wang, H Xin, C Zheng, L Li, Z Liu, Q Cao… - arXiv preprint arXiv …, 2023 - arxiv.org
Despite the success of large language models (LLMs), the task of theorem proving still
remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods …

Retrieval-augmented generation for ai-generated content: A survey

P Zhao, H Zhang, Q Yu, Z Wang, Y Geng, F Fu… - arXiv preprint arXiv …, 2024 - arxiv.org
The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …