Updated topicwizard citation to the preprint:

Author: x-tabdeveloping

paper.bib

@@ -170,13 +170,15 @@ @inproceedings{sentence_transformers
     abstract = "BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations ({\textasciitilde}65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods."
 }
   
-@software{topicwizard,
-  author = {Kardos, Márton},
-  month = nov,
-  title = {{topicwizard: Pretty and opinionated topic model visualization in Python}},
-  url = {https://github.com/x-tabdeveloping/topic-wizard},
-  version = {0.5.0},
-  year = {2023}
+@misc{topicwizard,
+      title={topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation}, 
+      author={Márton Kardos and Kenneth C. Enevoldsen and Kristoffer Laigaard Nielbo},
+      year={2025},
+      eprint={2505.13034},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.13034}, 
+      doi="10.48550/arXiv.2505.13034"
 }
 
 @article{discourse_analysis,