Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-4.0
 task_categories:
 - text-generation
 language:
@@ -14,7 +14,19 @@ size_categories:
 ---
 
 # Query Expansion Dataset
-Dataset for training search query expansion models with multiple semantic expansions.
+This dataset is designed for training search query expansion models that can generate multiple semantic expansions for a given query.
+
+## Purpose
+The goal of this dataset is to serve as input for training small language models (0.5B to 3B parameters) to act as query expander models in Retrieval-Augmented Generation (RAG) systems.
+
+Many advanced RAG systems currently use a query expansion component that makes external calls to large language models. While effective, this can add latency to the system. The purpose of this initiative is to enable the development of smaller, efficient query expander models that can perform this task without the added latency.
+
+This dataset is the first step. In the near future, I plan to release the trained query expander models as well.
+
+## Dataset Creation
+This dataset was created using a diverse set of state-of-the-art large language models. These LLMs were prompted with queries covering a wide range of topics and lengths, representing different user intents.
+
+The choice to use multiple LLMs was made to reduce the bias that might be introduced by using a single model. The broad spectrum of topics covered and the variety of query intents (informational, navigational, transactional, commercial) ensures the dataset is comprehensive and diverse.
 
 ## Structure
 
@@ -32,5 +44,18 @@ Dataset for training search query expansion models with multiple semantic expans
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("s-emanuilov/query_expansion")
-```
+dataset = load_dataset("s-emanuilov/query-expansion")
+```
+
+## License
+This dataset is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). 
+
+## Citation
+
+@dataset{query_expansion_dataset,
+  author = {Emanuilov, S},
+  title = {Query Expansion Dataset},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/s-emanuilov/query-expansion}},
+}