added citation

README.md

@@ -27,11 +27,11 @@ configs:
 The Agricultural Extension Corpus 1.1 is a compilation of 1655 official agricultural Extension documents (e.g., fact sheets, digital books) concerning water-related and sustainable agricultural practices research. 
 
 - **Homepage:** https://huggingface.co/datasets/msu-ceco/aec_v1
-- **Paper:** [More Information Needed]
+- **Paper:** [AgXQA: A benchmark for advanced Agricultural Extension question answering](https://doi.org/10.1016/j.compag.2024.109349)
 - **Curated by:** [DSI Lab](https://dsiweb.cse.msu.edu/)
 - **Point of Contact:** Dr. A.Pouyan Nejadhashemi ([email protected])
 - **Language(s) (NLP):** English (`en`)
-- **License:** [More Information Needed]
+<!-- - **License:** [More Information Needed] -->
 
 
 ## Uses
@@ -110,8 +110,26 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 
 **BibTeX:**
 
-[More Information Needed]
+```
+@article{KPODO2024109349,
+    title = {AgXQA: A benchmark for advanced Agricultural Extension question answering},
+    journal = {Computers and Electronics in Agriculture},
+    volume = {225},
+    pages = {109349},
+    year = {2024},
+    issn = {0168-1699},
+    doi = {https://doi.org/10.1016/j.compag.2024.109349},
+    url = {https://www.sciencedirect.com/science/article/pii/S0168169924007403},
+    author = {Josué Kpodo and Parisa Kordjamshidi and A. Pouyan Nejadhashemi},
+    keywords = {Agricultural Extension, Question-Answering, Annotated Dataset, Large Language Models, Zero-Shot Learning},
+    abstract = {Large language models (LLMs) have revolutionized various scientific fields in the past few years, thanks to their generative and extractive abilities. However, their applications in the Agricultural Extension (AE) domain remain sparse and limited due to the unique challenges of unstructured agricultural data. Furthermore, mainstream LLMs excel at general and open-ended tasks but struggle with domain-specific tasks. We proposed a novel QA benchmark dataset, AgXQA, for the AE domain to address these issues. We trained and evaluated our domain-specific LM, AgRoBERTa, which outperformed other mainstream encoder- and decoder- LMs, on the extractive QA downstream task by achieving an EM score of 55.15% and an F1 score of 78.89%. Besides automated metrics, we also introduced a custom human evaluation metric, AgEES, which confirmed AgRoBERTa’s performance, as demonstrated by a 94.37% agreement rate with expert assessments, compared to 92.62% for GPT 3.5. Notably, we conducted a comprehensive qualitative analysis, whose results provide further insights into the weaknesses and strengths of both domain-specific and general LMs when evaluated on in-domain NLP tasks. Thanks to this novel dataset and specialized LM, our research enhanced further development of specialized LMs for the agriculture domain as a whole and AE in particular, thus fostering sustainable agricultural practices through improved extractive question answering.}
+}
+
+```
 
 **APA:**
 
-[More Information Needed]
+```
+Kpodo, J., Kordjamshidi, P., & Nejadhashemi, A. P. (2024). AgXQA: A benchmark for advanced Agricultural Extension question answering. Computers and Electronics in Agriculture, 225, 109349. https://doi.org/10.1016/J.COMPAG.2024.109349 
+```
+