How is ChatGPT's behavior changing over time?

L Chen, M Zaharia, J Zou - Harvard Data Science Review, 2024 - hdsr.mitpress.mit.edu
GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services.
However, when and how these models are updated over time is opaque. Here, we evaluate …

A survey on LLM-generated text detection: Necessity, methods, and future directions

J Wu, S Yang, R Zhan, Y Yuan, LS Chao… - Computational …, 2025 - direct.mit.edu
The remarkable ability of large language models (LLMs) to comprehend, interpret, and
generate complex language has rapidly integrated LLM-generated text into various aspects …

AI for social science and social science of AI: A survey

R Xu, Y Sun, M Ren, S Guo, R Pan, H Lin, L Sun… - Information Processing …, 2024 - Elsevier
Recent advancements in artificial intelligence, particularly with the emergence of large
language models (LLMs), have sparked a rethinking of artificial general intelligence …

Agentcf: Collaborative learning with autonomous language agents for recommender systems

J Zhang, Y Hou, R **e, W Sun, J McAuley… - Proceedings of the …, 2024 - dl.acm.org
Recently, there has been an emergence of employing LLM-powered agents as believable
human proxies, based on their remarkable decision-making capability. However, existing …

Detecting multimedia generated by large ai models: A survey

L Lin, N Gupta, Y Zhang, H Ren, CH Liu, F Ding… - arxiv preprint arxiv …, 2024 - arxiv.org
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large
language models, has marked a new era where AI-generated multimedia is increasingly …

A survey on detection of llms-generated content

X Yang, L Pan, X Zhao, H Chen, L Petzold… - arxiv preprint arxiv …, 2023 - arxiv.org
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT
have led to an increase in synthetic content generation with implications across a variety of …

Learning and forgetting unsafe examples in large language models

J Zhao, Z Deng, D Madras, J Zou, M Ren - arxiv preprint arxiv:2312.12736, 2023 - arxiv.org
As the number of large language models (LLMs) released to the public grows, there is a
pressing need to understand the safety implications associated with these models learning …

Waterbench: Towards holistic evaluation of watermarks for large language models

S Tu, Y Sun, Y Bai, J Yu, L Hou, J Li - arxiv preprint arxiv:2311.07138, 2023 - arxiv.org
To mitigate the potential misuse of large language models (LLMs), recent research has
developed watermarking algorithms, which restrict the generation process to leave an …

Are large language models really good logical reasoners? a comprehensive evaluation and beyond

F Xu, Q Lin, J Han, T Zhao, J Liu… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Logical reasoning consistently plays a fundamental and significant role in the domains of
knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) …

Measuring social norms of large language models

Y Yuan, K Tang, J Shen, M Zhang, C Wang - arxiv preprint arxiv …, 2024 - arxiv.org
We present a new challenge to examine whether large language models understand social
norms. In contrast to existing datasets, our dataset requires a fundamental understanding of …