Mm-llms: Recent advances in multimodal large language models

D Zhang, Y Yu, J Dong, C Li, D Su, C Chu… - arxiv preprint arxiv …, 2024 - arxiv.org
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

Security and privacy challenges of large language models: A survey

BC Das, MH Amini, Y Wu - ACM Computing Surveys, 2024 - dl.acm.org
Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Survey on factuality in large language models: Knowledge, retrieval and domain-specificity

C Wang, X Liu, Y Yue, X Tang, T Zhang… - arxiv preprint arxiv …, 2023 - arxiv.org
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Sora: A review on background, technology, limitations, and opportunities of large vision models

Y Liu, K Zhang, Y Li, Z Yan, C Gao, R Chen… - arxiv preprint arxiv …, 2024 - arxiv.org
Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The
model is trained to generate videos of realistic or imaginative scenes from text instructions …

Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods

Y Cao, H Zhao, Y Cheng, T Shu, Y Chen… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …

Fairness in large language models: A taxonomic survey

Z Chu, Z Wang, W Zhang - ACM SIGKDD explorations newsletter, 2024 - dl.acm.org
Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …

Grounding and evaluation for large language models: Practical challenges and lessons learned (survey)

K Kenthapadi, M Sameki, A Taly - Proceedings of the 30th ACM SIGKDD …, 2024 - dl.acm.org
With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes
domains, ensuring the trustworthiness, safety, and observability of these systems has …

Having beer after prayer? measuring cultural bias in large language models

T Naous, MJ Ryan, A Ritter, W Xu - arxiv preprint arxiv:2305.14456, 2023 - arxiv.org
As the reach of large language models (LMs) expands globally, their ability to cater to
diverse cultural contexts becomes crucial. Despite advancements in multilingual …

The life cycle of large language models in education: A framework for understanding sources of bias

J Lee, Y Hicke, R Yu, C Brooks… - British Journal of …, 2024 - Wiley Online Library
Large language models (LLMs) are increasingly adopted in educational contexts to provide
personalized support to students and teachers. The unprecedented capacity of LLM‐based …

Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models

Z Lin, S Guan, W Zhang, H Zhang, Y Li… - Artificial Intelligence …, 2024 - Springer
Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …