EMNLP link

README.md

@@ -71,10 +71,10 @@ It generates definitions of Finnish words in context. Its input is the usage exa
 [aya-101, axolotl24 + dbnary](https://huggingface.co/ltg/aya-definition-ru-axolotl24st_dbnary)
 
 
-### Model Sources
+## More context
 
-- **Repository:** [MultilingualDefGen](https://github.com/ltgoslo/MultilingualDefGen)
-- **Paper:** [accepted to EMNLP 2025 Findings](https://arxiv.org/abs/2509.26181)
+- **Github repository:** [MultilingualDefGen](https://github.com/ltgoslo/MultilingualDefGen)
+- **Paper:** [EMNLP 2025 Findings](https://aclanthology.org/2025.findings-emnlp.1214/)
 
 ## Uses
 
@@ -125,14 +125,25 @@ BLEU, BERTScore
 **BibTeX:**
 
 ```
-@misc{fedorova2025explainingnovelsensesusing,
-      title={Explaining novel senses using definition generation with open language models}, 
-      author={Mariia Fedorova and Andrey Kutuzov and Francesco Periti and Yves Scherrer},
-      year={2025},
-      eprint={2509.26181},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2509.26181}, 
+@inproceedings{fedorova-etal-2025-explaining,
+    title = "Explaining novel senses using definition generation with open language models",
+    author = "Fedorova, Mariia  and
+      Kutuzov, Andrey  and
+      Periti, Francesco  and
+      Scherrer, Yves",
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.findings-emnlp.1214/",
+    pages = "22294--22302",
+    ISBN = "979-8-89176-335-7",
+    abstract = "We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL{'}24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts."
 }
 ```