5 citations · 11 across the 5 of their papers we have counts for
5 papers
Linear Spatial World Models Emerge in Large Language Models
Matthieu Tehenan, Christian Bolivar Moya, Tenghai Long +1
Large language models (LLMs) have demonstrated emergent abilities across diverse tasks, raising the question of whether they acquire internal world models. In this work, we investi…
Hierarchical Explanations for Video Action Recognition
Sadaf Gulshad, Teng Long, Nanne van Noord
To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods of…
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs
Teng Long, Yanshuai Cao, Jackie Chi Kit Cheung
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of na…
A Cross-Domain Transferable Neural Coherence Model
Peng Xu, Hamidreza Saghir, Jin Sung Kang +4
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain d…
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data
Teng Long, Ryan Lowe, Jackie Chi Kit Cheung +1
Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional informati…