Add link to leaderboard and standard reference

README.md

@@ -59,7 +59,7 @@ task_ids:
 - **Homepage:** [The official homepage of Språkbanken](https://spraakbanken.gu.se/resurser/superlim/)
 - **Repository:**
 - **Paper:**[SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models](https://gup.ub.gu.se/publication/299130?lang=sv)
-- **Leaderboard:** [To be implemented]
+- **Leaderboard:** https://lab.kb.se/leaderboard/
 - **Point of Contact:**[sb-info@svenska.gu.se](sb-info@svenska.gu.se)
 
 ### Dataset Summary
@@ -153,7 +153,46 @@ To cite as a whole, use the standard reference. If you use or reference individu
  
 Standard reference:
 
-To appear in EMNLP 2023, citation will come soon.
+Superlim: A Swedish Language Understanding Evaluation Benchmark (Berdicevskis et al., EMNLP 2023)
+
+```
+
+@inproceedings{berdicevskis-etal-2023-superlim,
+    title = "Superlim: A {S}wedish Language Understanding Evaluation Benchmark",
+    author = {Berdicevskis, Aleksandrs  and
+      Bouma, Gerlof  and
+      Kurtz, Robin  and
+      Morger, Felix  and
+      {\"O}hman, Joey  and
+      Adesam, Yvonne  and
+      Borin, Lars  and
+      Dann{\'e}lls, Dana  and
+      Forsberg, Markus  and
+      Isbister, Tim  and
+      Lindahl, Anna  and
+      Malmsten, Martin  and
+      Rekathati, Faton  and
+      Sahlgren, Magnus  and
+      Volodina, Elena  and
+      B{\"o}rjeson, Love  and
+      Hengchen, Simon  and
+      Tahmasebi, Nina},
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.506",
+    doi = "10.18653/v1/2023.emnlp-main.506",
+    pages = "8137--8153",
+    abstract = "We present Superlim, a multi-task NLP benchmark and analysis platform for evaluating Swedish language models, a counterpart to the English-language (Super)GLUE suite. We describe the dataset, the tasks, the leaderboard and report the baseline results yielded by a reference implementation. The tested models do not approach ceiling performance on any of the tasks, which suggests that Superlim is truly difficult, a desirable quality for a benchmark. We address methodological challenges, such as mitigating the Anglocentric bias when creating datasets for a less-resourced language; choosing the most appropriate measures; documenting the datasets and making the leaderboard convenient and transparent. We also highlight other potential usages of the dataset, such as, for instance, the evaluation of cross-lingual transfer learning.",
+}
+
+
+```
 
 Dataset references: