Update README.md

README.md

@@ -54,4 +54,46 @@ To obtain the final number of points, multiply each score with the respective po
 
 ## License and Use
 The problems and their translations are sourced from [IOL](https://ioling.org/), and are copyrighted by ©2003-2024 International Linguistics Olympiad.
-They may only be used for research purposes and evaluation, not training.
+They may only be used for research purposes and evaluation, not training.
+
+## Citation
+If you use this dataset, please cite it as follows:
+```
+@inproceedings{kocmi-etal-2025-findings-wmt25,
+    title = "Findings of the {WMT}25 Multilingual Instruction Shared Task: Persistent Hurdles in Reasoning, Generation, and Evaluation",
+    author = "Kocmi, Tom  and
+      Agrawal, Sweta  and
+      Artemova, Ekaterina  and
+      Avramidis, Eleftherios  and
+      Briakou, Eleftheria  and
+      Chen, Pinzhen  and
+      Fadaee, Marzieh  and
+      Freitag, Markus  and
+      Grundkiewicz, Roman  and
+      Hou, Yupeng  and
+      Koehn, Philipp  and
+      Kreutzer, Julia  and
+      Mansour, Saab  and
+      Perrella, Stefano  and
+      Proietti, Lorenzo  and
+      Riley, Parker  and
+      S{\'a}nchez, Eduardo  and
+      Schmidtova, Patricia  and
+      Shmatova, Mariya  and
+      Zouhar, Vil{\'e}m",
+    editor = "Haddow, Barry  and
+      Kocmi, Tom  and
+      Koehn, Philipp  and
+      Monz, Christof",
+    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.wmt-1.23/",
+    doi = "10.18653/v1/2025.wmt-1.23",
+    pages = "414--435",
+    ISBN = "979-8-89176-341-8",
+    abstract = "The WMT25 Multilingual Instruction Shared Task (MIST) introduces a benchmark to evaluate large language models (LLMs) across 30 languages. The benchmark covers five types of problems: machine translation, linguistic reasoning, open-ended generation, cross-lingual summarization, and LLM-as-a-judge.We provide automatic evaluation and collect human annotations, which highlight the limitations of automatic evaluation and allow further research into metric meta-evaluation. We run on our benchmark a diverse set of open- and closed-weight LLMs, providing a broad assessment of the multilingual capabilities of current LLMs. Results highlight substantial variation across sub-tasks and languages, revealing persistent challenges in reasoning, cross-lingual generation, and evaluation reliability. This work establishes a standardized framework for measuring future progress in multilingual LLM development."
+}
+```