A survey of knowledge-enhanced text generation

W Yu, C Zhu, Z Li, Z Hu, Q Wang, H Ji… - ACM Computing …, 2022 - dl.acm.org
The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Artificial intelligence and internet of things (AI-IoT) technologies in response to COVID-19 pandemic: A systematic review

JI Khan, J Khan, F Ali, F Ullah, J Bacha, S Lee - Ieee Access, 2022 - ieeexplore.ieee.org
The origin of the COVID-19 pandemic has given overture to redirection, as well as
innovation to many digital technologies. Even after the progression of vaccination efforts …

Bold: Dataset and metrics for measuring biases in open-ended language generation

J Dhamala, T Sun, V Kumar, S Krishna… - Proceedings of the …, 2021 - dl.acm.org
Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context. While these …

Artificial intelligence for science in quantum, atomistic, and continuum systems

X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y **e… - arxiv preprint arxiv …, 2023 - arxiv.org
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

COVID-19 literature knowledge graph construction and drug repurposing report generation

Q Wang, M Li, X Wang, N Parulian, G Han, J Ma… - arxiv preprint arxiv …, 2020 - arxiv.org
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …

Negative object presence evaluation (nope) to measure object hallucination in vision-language models

H Lovenia, W Dai, S Cahyawijaya, Z Ji… - arxiv preprint arxiv …, 2023 - arxiv.org
Object hallucination poses a significant challenge in vision-language (VL) models, often
leading to the generation of nonsensical or unfaithful responses with non-existent objects …

Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network

M Krenn, L Buffoni, B Coutinho, S Eppel… - Nature Machine …, 2023 - nature.com
A tool that could suggest new personalized research directions and ideas by taking insights
from the scientific literature could profoundly accelerate the progress of science. A field that …

Gaia: A fine-grained multimedia knowledge extraction system

M Li, A Zareian, Y Lin, X Pan, S Whitehead… - Proceedings of the …, 2020 - aclanthology.org
We present the first comprehensive, open source multimedia knowledge extraction system
that takes a massive stream of unstructured, heterogeneous multimedia data from various …

Predicting research trends with semantic and neural networks with an application in quantum physics

M Krenn, A Zeilinger - … of the National Academy of Sciences, 2020 - National Acad Sciences
The vast and growing number of publications in all disciplines of science cannot be
comprehended by a single human researcher. As a consequence, researchers have to …

[PDF][PDF] Learning to generate novel scientific directions with contextualized literature-based discovery

Q Wang, D Downey, H Ji… - arxiv preprint arxiv …, 2023 - blender.cs.illinois.edu
Abstract Literature-Based Discovery (LBD) aims to discover new scientific knowledge by
mining papers and generating hypotheses. Standard LBD is limited to predicting pairwise …