Update README.md

README.md

@@ -21,7 +21,7 @@ You will need to login with your hugginface token (```[HF-TOKEN]```, in the foll
 ### Model Description
 This model is fine-tuned on English datasets of sense definitions. Given a target word and a usage example, the model generates a sense definition for the target word in-context.
 
-You can find more details in the paper [Automatically Generated Definitions and their utility for Modeling Word Meaning](link) by Francesco Periti, David Alfter, Nina Tahmasebi.
+You can find more details in the paper [Automatically Generated Definitions and their utility for Modeling Word Meaning](https://aclanthology.org/2024.emnlp-main.776/) by Francesco Periti, David Alfter, Nina Tahmasebi.
 The repository of our project is [https://github.com/FrancescoPeriti/LlamaDictionary](https://github.com/FrancescoPeriti/LlamaDictionary).
 
 ## Uses
@@ -154,16 +154,21 @@ for row in dataset:
 
 ## Citation
 
-Francesco Periti, David Alfter, and Nina Tahmasebi. 2024. Automatically Generated Definitions and their utility for Modeling Word Meaning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami. Association for Computational Linguistics.
+Francesco Periti, David Alfter, and Nina Tahmasebi. 2024. [Automatically Generated Definitions and their utility for Modeling Word Meaning](https://aclanthology.org/2024.emnlp-main.776/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14008–14026, Miami, Florida, USA. Association for Computational Linguistics.
 
 **BibTeX:**
 ```
-@InProceedings{periti2024automatically,
-  author = {Francesco Periti and David Alfter and Nina Tahmasebi},
-  title = {{Automatically Generated Definitions and their Utility for Modeling Word Meaning}},
-  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
-  year = {2024},
-  address = {Miami, Florida},
-  publisher = {Association for Computational Linguistics}
+@inproceedings{periti2024automatically,
+    title = {{Automatically Generated Definitions and their utility for Modeling Word Meaning}},
+    author = "Periti, Francesco  and Alfter, David  and Tahmasebi, Nina",
+    editor = "Al-Onaizan, Yaser  and Bansal, Mohit  and Chen, Yun-Nung",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.emnlp-main.776",
+    pages = "14008--14026",
+    abstract = "Modeling lexical semantics is a challenging task, often suffering from interpretability pitfalls. In this paper, we delve into the generation of dictionary-like sense definitions and explore their utility for modeling word meaning. We fine-tuned two Llama models and include an existing T5-based model in our evaluation. Firstly, we evaluate the quality of the generated definitions on existing English benchmarks, setting new state-of-the-art results for the Definition Generation task. Next, we explore the use of definitions generated by our models as intermediate representations subsequently encoded as sentence embeddings. We evaluate this approach on lexical semantics tasks such as the Word-in-Context, Word Sense Induction, and Lexical Semantic Change, setting new state-of-the-art results in all three tasks when compared to unsupervised baselines.",
 }
 ```