metadata
language:
- en
- es
pretty_name: ' 💾🏋️💾 DataBench 💾🏋️💾'
tags:
- table-question-answering
- table
- qa
license: mit
task_categories:
- table-question-answering
- question-answering
configs:
- config_name: qa
data_files:
- data/001_Forbes/qa.parquet
- data/002_Titanic/qa.parquet
- data/003_Love/qa.parquet
- data/004_Taxi/qa.parquet
- data/005_NYC/qa.parquet
- data/006_London/qa.parquet
- data/007_Fifa/qa.parquet
- data/008_Tornados/qa.parquet
- data/009_Central/qa.parquet
- data/010_ECommerce/qa.parquet
- data/011_SF/qa.parquet
- data/012_Heart/qa.parquet
- data/013_Roller/qa.parquet
- data/014_Airbnb/qa.parquet
- data/015_Food/qa.parquet
- data/016_Holiday/qa.parquet
- data/017_Hacker/qa.parquet
- data/018_Staff/qa.parquet
- data/019_Aircraft/qa.parquet
- data/020_Real/qa.parquet
- data/021_Telco/qa.parquet
- data/022_Airbnbs/qa.parquet
- data/023_Climate/qa.parquet
- data/024_Salary/qa.parquet
- data/025_Data/qa.parquet
- data/026_Predicting/qa.parquet
- data/027_Supermarket/qa.parquet
- data/028_Predict/qa.parquet
- data/029_NYTimes/qa.parquet
- data/030_Professionals/qa.parquet
- data/031_Trustpilot/qa.parquet
- data/032_Delicatessen/qa.parquet
- data/033_Employee/qa.parquet
- data/034_World/qa.parquet
- data/035_Billboard/qa.parquet
- data/036_US/qa.parquet
- data/037_Ted/qa.parquet
- data/038_Stroke/qa.parquet
- data/039_Happy/qa.parquet
- data/040_Speed/qa.parquet
- data/041_Airline/qa.parquet
- data/042_Predict/qa.parquet
- data/043_Predict/qa.parquet
- data/044_IMDb/qa.parquet
- data/045_Predict/qa.parquet
- data/046_120/qa.parquet
- data/047_Bank/qa.parquet
- data/048_Data/qa.parquet
- data/049_Boris/qa.parquet
- data/050_ING/qa.parquet
- data/051_Pokemon/qa.parquet
- data/052_Professional/qa.parquet
- data/053_Patents/qa.parquet
- data/054_Joe/qa.parquet
- data/055_German/qa.parquet
- data/056_Emoji/qa.parquet
- data/057_Spain/qa.parquet
- data/058_US/qa.parquet
- data/059_Second/qa.parquet
- data/060_Bakery/qa.parquet
- data/061_Disneyland/qa.parquet
- data/062_Trump/qa.parquet
- data/063_Influencers/qa.parquet
- data/064_Clustering/qa.parquet
- data/065_RFM/qa.parquet
- config_name: 008_Tornados
data_files:
- split: full
path: data/008_Tornados/all.parquet
- split: lite
path: data/008_Tornados/sample.parquet
- config_name: data
data_files:
- split: 001_Forbes
path: data/001_Forbes/all.parquet
- split: 002_Titanic
path: data/002_Titanic/all.parquet
- split: 003_Love
path: data/003_Love/all.parquet
- split: 004_Taxi
path: data/004_Taxi/all.parquet
- split: 005_NYC
path: data/005_NYC/all.parquet
- split: 006_London
path: data/006_London/all.parquet
- split: 007_Fifa
path: data/007_Fifa/all.parquet
- split: 008_Tornados
path: data/008_Tornados/all.parquet
- split: 009_Central
path: data/009_Central/all.parquet
- split: 010_ECommerce
path: data/010_ECommerce/all.parquet
- split: 011_SF
path: data/011_SF/all.parquet
- split: 012_Heart
path: data/012_Heart/all.parquet
- split: 013_Roller
path: data/013_Roller/all.parquet
- split: 014_Airbnb
path: data/014_Airbnb/all.parquet
- split: 015_Food
path: data/015_Food/all.parquet
- split: 016_Holiday
path: data/016_Holiday/all.parquet
- split: 017_Hacker
path: data/017_Hacker/all.parquet
- split: 018_Staff
path: data/018_Staff/all.parquet
- split: 019_Aircraft
path: data/019_Aircraft/all.parquet
- split: 020_Real
path: data/020_Real/all.parquet
- split: 021_Telco
path: data/021_Telco/all.parquet
- split: 022_Airbnbs
path: data/022_Airbnbs/all.parquet
- split: 023_Climate
path: data/023_Climate/all.parquet
- split: 024_Salary
path: data/024_Salary/all.parquet
- split: 025_Data
path: data/025_Data/all.parquet
- split: 026_Predicting
path: data/026_Predicting/all.parquet
- split: 027_Supermarket
path: data/027_Supermarket/all.parquet
- split: 028_Predict
path: data/028_Predict/all.parquet
- split: 029_NYTimes
path: data/029_NYTimes/all.parquet
- split: 030_Professionals
path: data/030_Professionals/all.parquet
- split: 031_Trustpilot
path: data/031_Trustpilot/all.parquet
- split: 032_Delicatessen
path: data/032_Delicatessen/all.parquet
- split: 033_Employee
path: data/033_Employee/all.parquet
- split: 034_World
path: data/034_World/all.parquet
- split: 035_Billboard
path: data/035_Billboard/all.parquet
- split: 036_US
path: data/036_US/all.parquet
- split: 037_Ted
path: data/037_Ted/all.parquet
- split: 038_Stroke
path: data/038_Stroke/all.parquet
- split: 039_Happy
path: data/039_Happy/all.parquet
- split: 040_Speed
path: data/040_Speed/all.parquet
- split: 041_Airline
path: data/041_Airline/all.parquet
- split: 042_Predict
path: data/042_Predict/all.parquet
- split: 043_Predict
path: data/043_Predict/all.parquet
- split: 044_IMDb
path: data/044_IMDb/all.parquet
- split: 045_Predict
path: data/045_Predict/all.parquet
- split: '046_120'
path: data/046_120/all.parquet
- split: 047_Bank
path: data/047_Bank/all.parquet
- split: 048_Data
path: data/048_Data/all.parquet
- split: 049_Boris
path: data/049_Boris/all.parquet
- split: 050_ING
path: data/050_ING/all.parquet
- split: 051_Pokemon
path: data/051_Pokemon/all.parquet
- split: 052_Professional
path: data/052_Professional/all.parquet
- split: 053_Patents
path: data/053_Patents/all.parquet
- split: 054_Joe
path: data/054_Joe/all.parquet
- split: 055_German
path: data/055_German/all.parquet
- split: 056_Emoji
path: data/056_Emoji/all.parquet
- split: 057_Spain
path: data/057_Spain/all.parquet
- split: 058_US
path: data/058_US/all.parquet
- split: 059_Second
path: data/059_Second/all.parquet
- split: 060_Bakery
path: data/060_Bakery/all.parquet
- split: 061_Disneyland
path: data/061_Disneyland/all.parquet
- split: 062_Trump
path: data/062_Trump/all.parquet
- split: 063_Influencers
path: data/063_Influencers/all.parquet
- split: 064_Clustering
path: data/064_Clustering/all.parquet
- split: 065_RFM
path: data/065_RFM/all.parquet
- config_name: data_lite
data_files:
- split: 001_Forbes
path: data/001_Forbes/sample.parquet
- split: 002_Titanic
path: data/002_Titanic/sample.parquet
- split: 003_Love
path: data/003_Love/sample.parquet
- split: 004_Taxi
path: data/004_Taxi/sample.parquet
- split: 005_NYC
path: data/005_NYC/sample.parquet
- split: 006_London
path: data/006_London/sample.parquet
- split: 007_Fifa
path: data/007_Fifa/sample.parquet
- split: 008_Tornados
path: data/008_Tornados/sample.parquet
- split: 009_Central
path: data/009_Central/sample.parquet
- split: 010_ECommerce
path: data/010_ECommerce/sample.parquet
- split: 011_SF
path: data/011_SF/sample.parquet
- split: 012_Heart
path: data/012_Heart/sample.parquet
- split: 013_Roller
path: data/013_Roller/sample.parquet
- split: 014_Airbnb
path: data/014_Airbnb/sample.parquet
- split: 015_Food
path: data/015_Food/sample.parquet
- split: 016_Holiday
path: data/016_Holiday/sample.parquet
- split: 017_Hacker
path: data/017_Hacker/sample.parquet
- split: 018_Staff
path: data/018_Staff/sample.parquet
- split: 019_Aircraft
path: data/019_Aircraft/sample.parquet
- split: 020_Real
path: data/020_Real/sample.parquet
- split: 021_Telco
path: data/021_Telco/sample.parquet
- split: 022_Airbnbs
path: data/022_Airbnbs/sample.parquet
- split: 023_Climate
path: data/023_Climate/sample.parquet
- split: 024_Salary
path: data/024_Salary/sample.parquet
- split: 025_Data
path: data/025_Data/sample.parquet
- split: 026_Predicting
path: data/026_Predicting/sample.parquet
- split: 027_Supermarket
path: data/027_Supermarket/sample.parquet
- split: 028_Predict
path: data/028_Predict/sample.parquet
- split: 029_NYTimes
path: data/029_NYTimes/sample.parquet
- split: 030_Professionals
path: data/030_Professionals/sample.parquet
- split: 031_Trustpilot
path: data/031_Trustpilot/sample.parquet
- split: 032_Delicatessen
path: data/032_Delicatessen/sample.parquet
- split: 033_Employee
path: data/033_Employee/sample.parquet
- split: 034_World
path: data/034_World/sample.parquet
- split: 035_Billboard
path: data/035_Billboard/sample.parquet
- split: 036_US
path: data/036_US/sample.parquet
- split: 037_Ted
path: data/037_Ted/sample.parquet
- split: 038_Stroke
path: data/038_Stroke/sample.parquet
- split: 039_Happy
path: data/039_Happy/sample.parquet
- split: 040_Speed
path: data/040_Speed/sample.parquet
- split: 041_Airline
path: data/041_Airline/sample.parquet
- split: 042_Predict
path: data/042_Predict/sample.parquet
- split: 043_Predict
path: data/043_Predict/sample.parquet
- split: 044_IMDb
path: data/044_IMDb/sample.parquet
- split: 045_Predict
path: data/045_Predict/sample.parquet
- split: '046_120'
path: data/046_120/sample.parquet
- split: 047_Bank
path: data/047_Bank/sample.parquet
- split: 048_Data
path: data/048_Data/sample.parquet
- split: 049_Boris
path: data/049_Boris/sample.parquet
- split: 050_ING
path: data/050_ING/sample.parquet
- split: 051_Pokemon
path: data/051_Pokemon/sample.parquet
- split: 052_Professional
path: data/052_Professional/sample.parquet
- split: 053_Patents
path: data/053_Patents/sample.parquet
- split: 054_Joe
path: data/054_Joe/sample.parquet
- split: 055_German
path: data/055_German/sample.parquet
- split: 056_Emoji
path: data/056_Emoji/sample.parquet
- split: 057_Spain
path: data/057_Spain/sample.parquet
- split: 058_US
path: data/058_US/sample.parquet
- split: 059_Second
path: data/059_Second/sample.parquet
- split: 060_Bakery
path: data/060_Bakery/sample.parquet
- split: 061_Disneyland
path: data/061_Disneyland/sample.parquet
- split: 062_Trump
path: data/062_Trump/sample.parquet
- split: 063_Influencers
path: data/063_Influencers/sample.parquet
- split: 064_Clustering
path: data/064_Clustering/sample.parquet
- split: 065_RFM
path: data/065_RFM/sample.parquet
- config_name: semeval
data_files:
- split: train
path:
- data/001_Forbes/qa.parquet
- data/002_Titanic/qa.parquet
- data/003_Love/qa.parquet
- data/004_Taxi/qa.parquet
- data/005_NYC/qa.parquet
- data/006_London/qa.parquet
- data/007_Fifa/qa.parquet
- data/008_Tornados/qa.parquet
- data/009_Central/qa.parquet
- data/010_ECommerce/qa.parquet
- data/011_SF/qa.parquet
- data/012_Heart/qa.parquet
- data/013_Roller/qa.parquet
- data/014_Airbnb/qa.parquet
- data/015_Food/qa.parquet
- data/016_Holiday/qa.parquet
- data/017_Hacker/qa.parquet
- data/018_Staff/qa.parquet
- data/019_Aircraft/qa.parquet
- data/020_Real/qa.parquet
- data/021_Telco/qa.parquet
- data/022_Airbnbs/qa.parquet
- data/023_Climate/qa.parquet
- data/024_Salary/qa.parquet
- data/025_Data/qa.parquet
- data/026_Predicting/qa.parquet
- data/027_Supermarket/qa.parquet
- data/028_Predict/qa.parquet
- data/029_NYTimes/qa.parquet
- data/030_Professionals/qa.parquet
- data/031_Trustpilot/qa.parquet
- data/032_Delicatessen/qa.parquet
- data/033_Employee/qa.parquet
- data/034_World/qa.parquet
- data/035_Billboard/qa.parquet
- data/036_US/qa.parquet
- data/037_Ted/qa.parquet
- data/038_Stroke/qa.parquet
- data/039_Happy/qa.parquet
- data/040_Speed/qa.parquet
- data/041_Airline/qa.parquet
- data/042_Predict/qa.parquet
- data/043_Predict/qa.parquet
- data/044_IMDb/qa.parquet
- data/045_Predict/qa.parquet
- data/046_120/qa.parquet
- data/047_Bank/qa.parquet
- data/048_Data/qa.parquet
- data/049_Boris/qa.parquet
- split: test
path:
- data/050_ING/qa.parquet
- data/051_Pokemon/qa.parquet
- data/052_Professional/qa.parquet
- data/053_Patents/qa.parquet
- data/054_Joe/qa.parquet
- data/055_German/qa.parquet
- data/056_Emoji/qa.parquet
- data/057_Spain/qa.parquet
- data/058_US/qa.parquet
- data/059_Second/qa.parquet
- data/060_Bakery/qa.parquet
- data/061_Disneyland/qa.parquet
- data/062_Trump/qa.parquet
- data/063_Influencers/qa.parquet
- data/064_Clustering/qa.parquet
- data/065_RFM/qa.parquet
💾🏋️💾 DataBench 💾🏋️💾
This repository contains the original 65 datasets used for the paper Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs which appeared in LREC-COLING 2024.
Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular reasoning in question answering on tabular data. Although there are some available datasets to assess question answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs. To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
📚 Datasets
By clicking on each name in the table below, you will be able to explore each dataset.
| Name | Rows | Cols | Domain | Source (Reference) | |
|---|---|---|---|---|---|
| 1 | Forbes | 2668 | 17 | Business | Forbes |
| 2 | Titanic | 887 | 8 | Travel and Locations | Kaggle |
| 3 | Love | 373 | 35 | Social Networks and Surveys | Graphext |
| 4 | Taxi | 100000 | 20 | Travel and Locations | Kaggle |
| 5 | NYC Calls | 100000 | 46 | Business | City of New York |
| 6 | London Airbnbs | 75241 | 74 | Travel and Locations | Kaggle |
| 7 | Fifa | 14620 | 59 | Sports and Entertainment | Kaggle |
| 8 | Tornados | 67558 | 14 | Health | Kaggle |
| 9 | Central Park | 56245 | 6 | Travel and Locations | Kaggle |
| 10 | ECommerce Reviews | 23486 | 10 | Business | Kaggle |
| 11 | SF Police | 713107 | 35 | Social Networks and Surveys | US Gov |
| 12 | Heart Failure | 918 | 12 | Health | Kaggle |
| 13 | Roller Coasters | 1087 | 56 | Sports and Entertainment | Kaggle |
| 14 | Madrid Airbnbs | 20776 | 75 | Travel and Locations | Inside Airbnb |
| 15 | Food Names | 906 | 4 | Business | Data World |
| 16 | Holiday Package Sales | 4888 | 20 | Travel and Locations | Kaggle |
| 17 | Hacker News | 9429 | 20 | Social Networks and Surveys | Kaggle |
| 18 | Staff Satisfaction | 14999 | 11 | Business | Kaggle |
| 19 | Aircraft Accidents | 23519 | 23 | Health | Kaggle |
| 20 | Real Estate Madrid | 26026 | 59 | Business | Idealista |
| 21 | Telco Customer Churn | 7043 | 21 | Business | Kaggle |
| 22 | Airbnbs Listings NY | 37012 | 33 | Travel and Locations | Kaggle |
| 23 | Climate in Madrid | 36858 | 26 | Travel and Locations | AEMET |
| 24 | Salary Survey Spain 2018 | 216726 | 29 | Business | INE |
| 25 | Data Driven SEO | 62 | 5 | Business | Graphext |
| 26 | Predicting Wine Quality | 1599 | 12 | Business | Kaggle |
| 27 | Supermarket Sales | 1000 | 17 | Business | Kaggle |
| 28 | Predict Diabetes | 768 | 9 | Health | Kaggle |
| 29 | NYTimes World In 2021 | 52588 | 5 | Travel and Locations | New York Times |
| 30 | Professionals Kaggle Survey | 19169 | 64 | Business | Kaggle |
| 31 | Trustpilot Reviews | 8020 | 6 | Business | TrustPilot |
| 32 | Delicatessen Customers | 2240 | 29 | Business | Kaggle |
| 33 | Employee Attrition | 14999 | 11 | Business | Kaggle(modified) |
| 34 | World Happiness Report 2020 | 153 | 20 | Social Networks and Surveys | World Happiness |
| 35 | Billboard Lyrics | 5100 | 6 | Sports and Entertainment | Brown University |
| 36 | US Migrations 2012-2016 | 288300 | 9 | Social Networks and Surveys | US Census |
| 37 | Ted Talks | 4005 | 19 | Social Networks and Surveys | Kaggle |
| 38 | Stroke Likelihood | 5110 | 12 | Health | Kaggle |
| 39 | Happy Moments | 100535 | 11 | Social Networks and Surveys | Kaggle |
| 40 | Speed Dating | 8378 | 123 | Social Networks and Surveys | Kaggle |
| 41 | Airline Mentions X (former Twitter) | 14640 | 15 | Social Networks and Surveys | X (former Twitter) |
| 42 | Predict Student Performance | 395 | 33 | Business | Kaggle |
| 43 | Loan Defaults | 83656 | 20 | Business | SBA |
| 44 | IMDb Movies | 85855 | 22 | Sports and Entertainment | Kaggle |
| 45 | Spotify Song Popularity | 21000 | 19 | Sports and Entertainment | Spotify |
| 46 | 120 Years Olympics | 271116 | 15 | Sports and Entertainment | Kaggle |
| 47 | Bank Customer Churn | 7088 | 15 | Business | Kaggle |
| 48 | Data Science Salary Data | 742 | 28 | Business | Kaggle |
| 49 | Boris Johnson UK PM Tweets | 3220 | 34 | Social Networks and Surveys | X (former Twitter) |
| 50 | ING 2019 X Mentions | 7244 | 22 | Social Networks and Surveys | X (former Twitter) |
| 51 | Pokemon Features | 1072 | 13 | Business | Kaggle |
| 52 | Professional Map | 1227 | 12 | Business | Kern et al, PNAS'20 |
| 53 | Google Patents | 9999 | 20 | Business | BigQuery |
| 54 | Joe Biden Tweets | 491 | 34 | Social Networks and Surveys | X (former Twitter) |
| 55 | German Loans | 1000 | 18 | Business | Kaggle |
| 56 | Emoji Diet | 58 | 35 | Health | Kaggle |
| 57 | Spain Survey 2015 | 20000 | 45 | Social Networks and Surveys | CIS |
| 58 | US Polls 2020 | 3523 | 52 | Social Networks and Surveys | Brandwatch |
| 59 | Second Hand Cars | 50000 | 21 | Business | DataMarket |
| 60 | Bakery Purchases | 20507 | 5 | Business | Kaggle |
| 61 | Disneyland Customer Reviews | 42656 | 6 | Travel and Locations | Kaggle |
| 62 | Trump Tweets | 15039 | 20 | Social Networks and Surveys | X (former Twitter) |
| 63 | Influencers | 1039 | 14 | Social Networks and Surveys | X (former Twitter) |
| 64 | Clustering Zoo Animals | 101 | 18 | Health | Kaggle |
| 65 | RFM Analysis | 541909 | 8 | Business | UCI ML |
🏗️ Folder structure
Each folder represents one dataset. You will find the following files within:
- all.parquet: the processed data, with each column tagged with our typing system, in parquet.
- qa.parquet: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite)
- sample.parquet: sample containing 20 rows of the original dataset (DataBench lite)
- info.yml: additional information about the dataset
🗂️ Column typing system
In an effort to map the stage for later analysis, we have categorized the columns by type. This information allows us to segment different kinds of data so that we can subsequently analyze the model's behavior on each column type separately. All parquet files have been casted to their smallest viable data type using the open source Lector reader.
What this means is that in the data types we have more granular information that allows us to know if the column contains NaNs or not (following panda’s convention of Int vs int), as well as whether small numerical values contain negatives (Uint vs int) and their range. We also have dates with potential timezone information (although for now they’re all UTC), as well as information about categories’ cardinality coming from the arrow types.
In the table below you can see all the data types assigned to each column, as well as the number of columns for each type. The most common data types are numbers and categories with 1336 columns of the total of 1615 included in DataBench. These are followed by some other more rare types as urls, booleans, dates or lists of elements.
| Type | Columns | Example |
|---|---|---|
| number | 788 | 55 |
| category | 548 | apple |
| date | 50 | 1970-01-01 |
| text | 46 | A red fox ran... |
| url | 31 | google.com |
| boolean | 18 | True |
| list[number] | 14 | [1,2,3] |
| list[category] | 112 | [apple, orange, banana] |
| list[url] | 8 | [google.com, apple.com] |
🔗 Reference
You can download the paper here.
If you use this resource, please use the following reference:
@inproceedings{oses-etal-2024-databench,
title = "Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs",
author = "Jorge Osés Grijalba and Luis Alfonso Ureña-López and
Eugenio Martínez Cámara and Jose Camacho-Collados",
booktitle = "Proceedings of LREC-COLING 2024",
year = "2024",
address = "Turin, Italy"
}