added GH repo link and paper link

README.md

@@ -95,13 +95,14 @@ configs:
 ## Dataset Description
 
 - **Homepage:** https://huggingface.co/datasets/msu-ceco/agxqa_v1
-- **Paper:** [TO-DO]()
+- **Paper:**  [AgXQA: A benchmark for advanced Agricultural Extension question answering](https://doi.org/10.1016/j.compag.2024.109349)
+- **GitHub:** [agxqa_benchmark_v1](https://github.com/MSU-CECO/agxqa_benchmark_v1/)
 - **Point of Contact:** Dr. A.Pouyan Nejadhashemi (pouyan@msu.edu)
 
 ### Dataset Summary
 
 The Agricultural eXtension Question Answering Dataset (AgXQA 1.1) is a small-scale, SQuAD-like QA dataset targeting the Agriculture Extension domain. 
-Version 1.1 currently contains 2.1K+ questions related to irrigation topics across the US, with a focus on the Midwest since our crops of interest were mainly soybean and corn.
+Version 1.1 currently contains 2.1K+ questions related to irrigation topics across the US, focusing on the Midwest since our crops of interest were mainly soybean and corn.
 
 ### Supported Tasks and Leaderboards
 
@@ -202,18 +203,18 @@ Our main guidelines can be summarized as follows:
   - predicates
   - phrases (nouns, verbs, adjectives and adverbials)
 - Quality control: Domain experts reviewed and validated the QA pairs to ensure accuracy and relevance. This review was conducted weekly on 50% of the annotated batch (randomly selected) for that week.
-- Diversity and Coverage: Since the crops of interest (soybean and corn) are mostly grown in the Midwest states of the USA, most of the QA pairs cover those states. However, the dataset also includes general irrigation QA pairs, that are applicable in most states.
+Diversity and Coverage: Since the crops of interest (soybeans and corn) are mostly grown in the Midwest states of the USA, most of the QA pairs cover those states. However, the dataset also includes general irrigation QA pairs that are applicable in most states.
 - Ethical considerations: To maintain transparency and credibility, we cited the original authors of the annotated paragraphs for each QA pair. Please see the annotated example provided above.
 
-For more information on the annotation process, please refer to [here (TO_DO)]().
+For more information on the annotation process, please refer to the accompanying [paper](https://doi.org/10.1016/j.compag.2024.109349).
 
 #### Who are the annotators?
 
-There were three annotators in total, two with a background in agricultural topics. Two experts in water and irrigation research hired them and supervised their annotations.
+There were three annotators in total, two with a background in agricultural and environmental topics. Two experts in water and irrigation research hired them and supervised their annotations.
 
 ### Personal and Sensitive Information
 
-* Some of the original paragraphs contained extension educators' names and email addresses, but these have been analyzed accordingly. In other words, they have been replaced with `x` 's in our dataset.
+* Some original paragraphs contained extension educators' names and email addresses, but these have been analyzed accordingly. In other words, they have been replaced with `x` 's in our dataset.
 * For each paragraph, we referenced the main article, where the context was extracted. 
 
 ## Considerations for Using the Data
@@ -224,7 +225,7 @@ There were three annotators in total, two with a background in agricultural topi
 
 ### Discussion of Biases
 
-* Version 1.1 is small and only contains irrigation-related topics, so we suggested not using it in production since, in the real world, agriculture-based questions require temporal and geospatial information, which is not covered yet. 
+* Version 1.1 is quite small, compared to most QA datasets, and only contains irrigation-related topics, so we suggested not using it in production since, in the real world, agriculture-based questions require temporal and geospatial information, which is not covered yet. 
 * We found three paragraphs that contained URLs (links to an Extension YouTube video and a decision support tool). These are outliers and do not necessarily provide implicit answers. They will be removed in version 2.  
 
 ### Other Known Limitations
@@ -239,9 +240,35 @@ The dataset is distributed under the [TO-DO] license.
 
 ### Citation Information
 
-[TO-DO]
 
+**BibTeX:**
 
+```
+@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:**
+
+```
+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 
+```
+
+<!-- 
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