Initial commit

README.md

@@ -70,7 +70,7 @@ model-runs/
     ├── bertopic/
     ├── ctm/
     └── mallet/
-````
+```
 
 Each folder contains model-specific artifacts (see below).
 
@@ -119,14 +119,24 @@ If you use this dataset, please cite:
 
 ```bibtex
 @inproceedings{hoyle-etal-2025-proxann,
-  title = "{P}rox{A}nn: Use-Oriented Evaluations of Topic Models and Document Clustering",
-  author = "Hoyle, Alexander Miserlis and Calvo-Bartolom{\\'e}, Lorena and Boyd-Graber, Jordan Lee and Resnik, Philip",
-  booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
-  year = "2025",
-  address = "Vienna, Austria",
-  publisher = "Association for Computational Linguistics",
-  url = "https://aclanthology.org/2025.acl-long.772/",
-  doi = "10.18653/v1/2025.acl-long.772",
-  pages = "15872--15897"
+    title = "{P}rox{A}nn: Use-Oriented Evaluations of Topic Models and Document Clustering",
+    author = "Hoyle, Alexander Miserlis  and
+      Calvo-Bartolom{\'e}, Lorena  and
+      Boyd-Graber, Jordan Lee  and
+      Resnik, Philip",
+    editor = "Che, Wanxiang  and
+      Nabende, Joyce  and
+      Shutova, Ekaterina  and
+      Pilehvar, Mohammad Taher",
+    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.acl-long.772/",
+    doi = "10.18653/v1/2025.acl-long.772",
+    pages = "15872--15897",
+    ISBN = "979-8-89176-251-0",
+    abstract = "Topic models and document-clustering evaluations either use automated metrics that align poorly with human preferences, or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators{---}or an LLM-based proxy{---}review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxy is statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations."
 }
 ```