Update README.md

README.md

@@ -79,11 +79,29 @@ HiFi-KPI can be used for:
 ## Citation
 If you use HiFi-KPI in your research, please cite:
 ```
-@article{aavang2025hifi,
-  title={HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings},
-  author={Aavang, Rasmus and Rizzi, Giovanni and Bøggild, Rasmus and Iolov, Alexandre and Zhang, Mike and Bjerva, Johannes},
-  journal={arXiv preprint arXiv:2502.15411},
-  year={2025}
+@inproceedings{aavang-etal-2026-hifi,
+    title = "{H}i{F}i-{KPI}: A Dataset for Hierarchical {KPI} Extraction from Earnings Filings",
+    author = "Aavang, Rasmus T.  and
+      Rizzi, Giovanni  and
+      Tjalk-B{\o}ggild, Rasmus  and
+      Iolov, Alexandre  and
+      Zhang, Mike  and
+      Bjerva, Johannes",
+    editor = "Piperidis, Stelios  and
+      Bel, N{\'u}ria  and
+      van den Heuvel, Henk  and
+      Ide, Nancy  and
+      Krek, Simon  and
+      Toral, Antonio",
+    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
+    month = may,
+    year = "2026",
+    address = "Palma de Mallorca, Spain",
+    publisher = "ELRA Language Resource Association",
+    url = "https://aclanthology.org/2026.lrec-1.30/",
+    doi = "10.63317/2nbsp7zzfb3g",
+    pages = "441--455",
+    abstract = "Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Business Reporting Language (iXBRL) is mandated for public financial filings. Yet, its complex, fine-grained taxonomy limits the cross-company transferability of tagged Key Performance Indicators (KPIs). To address this, we introduce the Hierarchical Financial Key Performance Indicator (HiFi-KPI) dataset, a large-scale corpus of 1.65M paragraphs and 198k unique, hierarchically organized labels linked to iXBRL taxonomies. HiFi-KPI supports multiple tasks and we evaluate three: KPI classification, KPI extraction, and structured KPI extraction. For rapid evaluation, we also release HiFi-KPI-Lite, a manually curated 2.5K-instance subset. Baselines on HiFi-KPI-Lite show that encoder-based models achieve over 0.906 macro-F1 on classification, while Large Language Models (LLMs) reach 0.440 F1 on structured extraction. Finally, a qualitative analysis reveals that extraction errors primarily relate to dates. We open-source all code and data at Anonymous."
 }
 
 ```