DROP: Benchmarking Comprehension and Reasoning
DROP Dataset: Evaluating Reading Comprehension and Reasoning Skills
By drop (From Huggingface) [source]
About this dataset
The DROP (A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs) dataset is a comprehensive and extensively crowdsourced benchmark that aims to assess the comprehension and reasoning capabilities of systems. It provides a standardized evaluation platform for language models by presenting them with 96,000 carefully constructed questions. These questions have been generated through crowd workers and adversarial techniques to ensure their complexity in challenging the systems.
The dataset consists of several key columns. The passage column contains paragraphs of text serving as contextual information for each question. These passages are carefully selected to introduce diverse topics, writing styles, and levels of complexity.
Additionally, the answers_spans column provides specific spans within each passage that contain the answers to the corresponding questions. These answer spans assist in evaluating a system's ability to locate relevant information within given passages accurately.
By using this extensive dataset, researchers can better assess how well their models comprehend complex text passages while also evaluating their reasoning capabilities when presented with nuanced questions requiring discrete thinking processes. The DROP dataset empowers researchers in developing advanced language models capable of accurate comprehension as well as complex reasoning over paragraphs of text across various domains
Research Ideas
- Training Comprehension and Reasoning Models: The DROP dataset can be used to train and evaluate comprehension and reasoning models. With its large-scale benchmark of 96,000 questions, it provides a diverse set of examples for the models to learn from.
- Evaluating Language Understanding: Since the dataset requires discrete reasoning over paragraphs of text, it can be used to evaluate the language understanding capabilities of various natural language processing models. It challenges the models to understand complex textual information and reason effectively.
- Benchmarking Performance: The DROP dataset can serve as a benchmark for comparing the performance of different comprehension and reasoning models. Researchers and developers can use this dataset to assess their model's accuracy, efficiency, and generalizability in solving tasks that involve reading comprehension and logical reasoning over textual passages
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: validation.csv
| Column name |
Description |
| passage |
This column contains paragraphs of text that serve as context for answering questions. (Text) |
| answers_spans |
This column contains spans of text within the passage that provide the answers to the corresponding questions. (Text) |
File: train.csv
| Column name |
Description |
| passage |
This column contains paragraphs of text that serve as context for answering questions. (Text) |
| answers_spans |
This column contains spans of text within the passage that provide the answers to the corresponding questions. (Text) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit drop (From Huggingface).