A survey on neural topic models: methods, applications, and challenges
Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …
proportions of documents in an unsupervised fashion. They have been widely used in …
AKEW: Assessing knowledge editing in the wild
Abstract Knowledge editing injects knowledge updates into language models to keep them
correct and up-to-date. However, its current evaluations deviate significantly from practice …
correct and up-to-date. However, its current evaluations deviate significantly from practice …
On the affinity, rationality, and diversity of hierarchical topic modeling
Hierarchical topic modeling aims to discover latent topics from a corpus and organize them
into a hierarchy to understand documents with desirable semantic granularity. However …
into a hierarchy to understand documents with desirable semantic granularity. However …
Infoctm: A mutual information maximization perspective of cross-lingual topic modeling
Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
MetaPro 2.0: Computational metaphor processing on the effectiveness of anomalous language modeling
Metaphor interpretation is a difficult task in natural language understanding. The
development of relevant techniques in this domain is slow, mostly because of the lack of …
development of relevant techniques in this domain is slow, mostly because of the lack of …
Towards the topmost: A topic modeling system toolkit
Topic models have been proposed for decades with various applications and recently
refreshed by the neural variational inference. However, these topic models adopt totally …
refreshed by the neural variational inference. However, these topic models adopt totally …
FASTopic: A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm
Topic models have been evolving rapidly over the years, from conventional to recent neural
models. However, existing topic models generally struggle with either effectiveness …
models. However, existing topic models generally struggle with either effectiveness …
Topic Modeling as Multi-Objective Contrastive Optimization
Recent representation learning approaches enhance neural topic models by optimizing the
weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and …
weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and …
Weak-to-Strong Backdoor Attack for Large Language Models
Despite being widely applied due to their exceptional capabilities, Large Language Models
(LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce …
(LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce …
Gradient-boosted decision tree for listwise context model in multimodal review helpfulness prediction
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on
predicted helpfulness scores and has been widely applied in e-commerce via presenting …
predicted helpfulness scores and has been widely applied in e-commerce via presenting …