Update README.md

README.md

@@ -13,6 +13,7 @@ pretty_name: DharmaBench
 <!-- Provide a quick summary of the dataset. -->
 
 ## Dataset Details
+See the full paper [here!](https://aclanthology.org/2025.ijcnlp-long.114/)
 
 ### Dataset Description
 
@@ -139,11 +140,57 @@ DharmaBench is best used for comparative evaluation and fine-tuning in controlle
 
 **BibTeX:**
 
-[More Information Needed]
+If you use DharmaBench in your research, please cite:
+
+```bibtex
+@inproceedings{hashiloni-etal-2025-dharmabench,
+    title = "{D}harma{B}ench: Evaluating Language Models on Buddhist Texts in {S}anskrit and {T}ibetan",
+    author = "Hashiloni, Kai Golan  and
+      Cohen, Shay  and
+      Shina, Asaf  and
+      Yang, Jingyi  and
+      Zwebner, Orr Meir  and
+      Bajetta, Nicola  and
+      Bilitski, Guy  and
+      Sund{\'e}n, Rebecca  and
+      Maduel, Guy  and
+      Conlon, Ryan  and
+      Barzilai, Ari  and
+      Mass, Daniel  and
+      Jia, Shanshan  and
+      Naaman, Aviv  and
+      Choden, Sonam  and
+      Jamtsho, Sonam  and
+      Qu, Yadi  and
+      Isaacson, Harunaga  and
+      Wangchuk, Dorji  and
+      Fine, Shai  and
+      Almogi, Orna  and
+      Bar, Kfir",
+    editor = "Inui, Kentaro  and
+      Sakti, Sakriani  and
+      Wang, Haofen  and
+      Wong, Derek F.  and
+      Bhattacharyya, Pushpak  and
+      Banerjee, Biplab  and
+      Ekbal, Asif  and
+      Chakraborty, Tanmoy  and
+      Singh, Dhirendra Pratap",
+    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
+    month = dec,
+    year = "2025",
+    address = "Mumbai, India",
+    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.ijcnlp-long.114/",
+    pages = "2088--2110",
+    ISBN = "979-8-89176-298-5",
+    abstract = "We assess the capabilities of large language models on tasks involving Buddhist texts written in Sanskrit and Classical Tibetan{---}two typologically distinct, low-resource historical languages. To this end, we introduce DharmaBench, a benchmark suite comprising 13 classification and detection tasks grounded in Buddhist textual traditions: six in Sanskrit and seven in Tibetan, with four shared across both. The tasks are curated from scratch, tailored to the linguistic and cultural characteristics of each language. We evaluate a range of models, from proprietary systems like GPT-4o to smaller, domain-specific open-weight models, analyzing their performance across tasks and languages. All datasets and code are publicly released, under the CC-BY-4 License and the Apache-2.0 License respectively, to support research on historical language processing and the development of culturally inclusive NLP systems."
+}
+```
 
 **APA:**
 
-[More Information Needed]
+DharmaBench: Evaluating Language Models on Buddhist Texts in Sanskrit and Tibetan (Hashiloni et al., IJCNLP-AACL 2025)
 
 
 ## Dataset Card Authors