Update README.md

README.md

@@ -7,13 +7,13 @@ language:
 size_categories:
 - 100M<n<1B
 ---
-# COMPLEXTEMPQA Dataset
+# ComplexTempQA Dataset
 
-COMPLEXTEMPQA is a large-scale dataset designed for complex temporal question answering (TQA). It consists of over 100 million question-answer pairs, making it one of the most extensive datasets available for TQA. The dataset is generated using data from Wikipedia and Wikidata and spans questions over a period of 36 years (1987-2023).
+ComplexTempQA is a large-scale dataset designed for complex temporal question answering (TQA). It consists of over 100 million question-answer pairs, making it one of the most extensive datasets available for TQA. The dataset is generated using data from Wikipedia and Wikidata and spans questions over a period of 36 years (1987-2023).
 
 ## Dataset Description
 
-COMPLEXTEMPQA categorizes questions into three main types:
+ComplexTempQA categorizes questions into three main types:
 - Attribute Questions
 - Comparison Questions
 - Counting Questions
@@ -49,7 +49,7 @@ Each question in the dataset is accompanied by detailed metadata, including:
 ## Dataset Characteristics
 
 ### Size
-COMPLEXTEMPQA comprises over 100 million question-answer pairs, focusing on events, entities, and time periods from 1987 to 2023.
+ComplexTempQA comprises over 100 million question-answer pairs, focusing on events, entities, and time periods from 1987 to 2023.
 
 ### Complexity
 Questions require advanced reasoning skills, including multi-hop question answering, temporal aggregation, and across-time comparisons.
@@ -63,7 +63,7 @@ The dataset has been evaluated for readability, ease of answering before and aft
 ## Usage
 
 ### Evaluation and Training
-COMPLEXTEMPQA can be used for:
+ComplexTempQA can be used for:
 - Evaluating the temporal reasoning capabilities of large language models (LLMs)
 - Fine-tuning language models for better temporal understanding
 - Developing and testing retrieval-augmented generation (RAG) systems
@@ -75,7 +75,7 @@ The dataset supports research in:
 - Language understanding
 
 ### Adaptation and Continual Learning
-COMPLEXTEMPQA's temporal metadata facilitates the development of online adaptation and continual training approaches for LLMs, aiding in the exploration of time-based learning and evaluation.
+ComplexTempQA's temporal metadata facilitates the development of online adaptation and continual training approaches for LLMs, aiding in the exploration of time-based learning and evaluation.
 
 ## Access