Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs …
Y Duan, C Chau, Z Wang… - Advances in Neural …, 2024 - proceedings.neurips.cc
The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …
Z Wang, H Ji - Proceedings of the AAAI Conference on Artificial …, 2022 - ojs.aaai.org
State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited …
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a …
An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain. However, most, if not all, previous works only …
O Eberle, S Brandl, J Pilot… - Proceedings of the 60th …, 2022 - aclanthology.org
Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language models …
N Xi, S Zhao, H Wang, C Liu, B Qin, T Liu - arXiv preprint arXiv …, 2023 - arxiv.org
Decoding text stimuli from cognitive signals (eg fMRI) enhances our understanding of the human language system, paving the way for building versatile Brain-Computer Interface …
We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation. This corpus contains …
The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift …