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question
string | answer
string | type
string | columns_used
string | column_types
string | sample_answer
string |
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Is the person with the highest net worth self-made? | True | boolean | ['finalWorth', 'selfMade'] | ['number[uint32]', 'boolean'] | False |
Does the youngest billionaire identify as male? | True | boolean | ['age', 'gender'] | ['number[UInt8]', 'category'] | True |
Is the city with the most billionaires in the United States? | True | boolean | ['city', 'country'] | ['category', 'category'] | True |
Is there a non-self-made billionaire in the top 5 ranks? | True | boolean | ['rank', 'selfMade'] | ['number[uint16]', 'boolean'] | False |
Does the oldest billionaire have a philanthropy score of 5? | False | boolean | ['age', 'philanthropyScore'] | ['number[UInt8]', 'number[UInt8]'] | False |
What is the age of the youngest billionaire? | 19.0 | number | ['age'] | ['number[UInt8]'] | 32.0 |
How many billionaires are there from the 'Technology' category? | 343 | number | ['category'] | ['category'] | 0 |
What's the total worth of billionaires in the 'Automotive' category? | 583600 | number | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | 0 |
How many billionaires have a philanthropy score above 3? | 25 | number | ['philanthropyScore'] | ['number[UInt8]'] | 0 |
What's the rank of the wealthiest non-self-made billionaire? | 3 | number | ['selfMade', 'rank'] | ['boolean', 'number[uint16]'] | 288 |
Which category does the richest billionaire belong to? | Automotive | category | ['finalWorth', 'category'] | ['number[uint32]', 'category'] | Food & Beverage |
What's the country of origin of the oldest billionaire? | United States | category | ['age', 'country'] | ['number[UInt8]', 'category'] | United Kingdom |
What's the gender of the billionaire with the highest philanthropy score? | M | category | ['philanthropyScore', 'gender'] | ['number[UInt8]', 'category'] | M |
What's the source of wealth for the youngest billionaire? | drugstores | category | ['age', 'source'] | ['number[UInt8]', 'category'] | fintech |
What is the title of the billionaire with the lowest rank? | null | category | ['rank', 'title'] | ['number[uint16]', 'category'] | null |
List the top 3 countries with the most billionaires. | ['United States', 'China', 'India'] | list[category] | ['country'] | ['category'] | ['United States', 'China', 'Brazil'] |
List the top 5 sources of wealth for billionaires. | ['real estate', 'investments', 'pharmaceuticals', 'diversified', 'software'] | list[category] | ['source'] | ['category'] | ['diversified', 'media, automotive', 'Semiconductor materials', 'WeWork', 'beverages'] |
List the top 4 cities where the youngest billionaires live. | [nan, 'Los Angeles', 'Jiaozuo', 'Oslo'] | list[category] | ['age', 'city'] | ['number[UInt8]', 'category'] | ['San Francisco', 'New York', 'Wuhan', 'Bangalore'] |
List the bottom 3 categories with the fewest billionaires. | ['Logistics', 'Sports', 'Gambling & Casinos'] | list[category] | ['category'] | ['category'] | ['Service', 'Fashion & Retail', 'Manufacturing'] |
List the bottom 2 countries with the least number of billionaires. | ['Colombia', 'Andorra'] | list[category] | ['country'] | ['category'] | ['Canada', 'Egypt'] |
List the top 5 ranks of billionaires who are not self-made. | [3, 10, 14, 16, 18] | list[number] | ['selfMade', 'rank'] | ['boolean', 'number[uint16]'] | [288, 296, 509, 523, 601] |
List the bottom 3 ages of billionaires who have a philanthropy score of 5. | [48.0, 83.0, 83.0] | list[number] | ['philanthropyScore', 'age'] | ['number[UInt8]', 'number[UInt8]'] | [] |
List the top 6 final worth values of billionaires in the 'Technology' category. | [171000, 129000, 111000, 107000, 106000, 91400] | list[number] | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | [] |
List the bottom 4 ranks of female billionaires. | [14, 18, 21, 30] | list[number] | ['gender', 'rank'] | ['category', 'number[uint16]'] | [] |
List the top 2 final worth values of billionaires in the 'Automotive' category. | [219000, 44800] | list[number] | ['category', 'finalWorth'] | ['category', 'number[uint32]'] | [] |
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Did any children below the age of 18 survive? | True | boolean | [Age, Survived] | ['number[UInt8]', 'boolean'] | True |
Were there any passengers who paid a fare of more than $500? | True | boolean | [Fare] | ['number[double]'] | False |
Is every passenger's name unique? | True | boolean | [Name] | ['text'] | True |
Were there any female passengers in the 3rd class who survived? | True | boolean | [Sex, Pclass, Survived] | ['category', 'number[uint8]', 'boolean'] | True |
How many unique passenger classes are present in the dataset? | 3 | number | [Pclass] | ['number[uint8]'] | 3 |
What's the maximum age of the passengers? | 80.0 | number | [Age] | ['number[UInt8]'] | 69.0 |
How many passengers boarded without any siblings or spouses? | 604 | number | [Siblings_Spouses Aboard] | ['number[uint8]'] | 12 |
On average, how much fare did the passengers pay? | 32.31 | number | [Fare] | ['number[double]'] | 23.096459999999997 |
Which passenger class has the highest number of survivors? | 1 | category | [Pclass, Survived] | ['number[uint8]', 'boolean'] | 3 |
What's the most common gender among the survivors? | female | category | [Sex, Survived] | ['category', 'boolean'] | female |
Among those who survived, which fare range was the most common: (0-50, 50-100, 100-150, 150+)? | 0-50 | category | [Fare, Survived] | ['number[double]', 'boolean'] | 0-50 |
What's the most common age range among passengers: (0-18, 18-30, 30-50, 50+)? | 18-30 | category | [Age] | ['number[UInt8]'] | 18-30 |
Name the top 3 passenger classes by survival rate. | [1, 2, 3] | list[category] | [Pclass, Survived] | ['number[uint8]', 'boolean'] | [1, 3, 2] |
Could you list the bottom 3 fare ranges by number of survivors: (0-50, 50-100, 100-150, 150+)? | ['50-100', '150+', '100-150'] | list[category] | [Fare, Survived] | ['number[double]', 'boolean'] | [50-100, 150+, 100-150] |
What is the top 4 age ranges('30-50', '18-30', '0-18', '50+') with the highest number of survivors? | ['30-50', '18-30', '0-18', '50+'] | list[category] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [30-50, 18-30, 0-18, 50+] |
What are the top 2 genders by average fare paid? | ['female', 'male'] | list[category] | [Sex, Fare] | ['category', 'number[double]'] | [female, male] |
What are the oldest 3 ages among the survivors? | [24.0, 22.0, 27.0] | list[number] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [56.0, 47.0, 42.0] |
Which are the top 4 fares paid by survivors? | [13.0, 26.0, 7.75, 10.5] | list[number] | [Fare, Survived] | ['number[double]', 'boolean'] | [133.65, 39.0, 35.5, 30.5] |
Could you list the youngest 3 ages among the survivors? | [53.0, 55.0, 11.0] | list[number] | [Age, Survived] | ['number[UInt8]', 'boolean'] | [14.0, 24.0, 28.0] |
Which are the bottom 4 fares among those who didn't survive? | [90.0, 12.275, 9.35, 10.5167] | list[number] | [Fare, Survived] | ['number[double]', 'boolean'] | [13.0, 7.75, 11.5, 10.1708] |
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Is the average age of the respondents above 30? | True | boolean | ['What is your age? ๐ถ๐ป๐ต๐ป'] | ['number[uint8]'] | True |
Are there more single individuals than married ones in the dataset? | True | boolean | ['What is your civil status? ๐'] | ['category'] | False |
Do the majority of respondents have a height greater than 170 cm? | True | boolean | [What's your height? in cm ๐] | ['number[uint8]'] | True |
Is the most frequent hair color black? | False | boolean | ['What is your hair color? ๐ฉ๐ฆฐ๐ฑ๐ฝ'] | ['category'] | False |
How many unique nationalities are present in the dataset? | 13 | number | [What's your nationality?"]" | ['category'] | 1 |
What is the average gross annual salary? | 56332.81720430108 | number | ['Gross annual salary (in euros) ๐ธ'] | ['number[UInt32]'] | 62710.0 |
How many respondents wear glasses all the time? | 0 | number | ['How often do you wear glasses? ๐'] | ['category'] | 0 |
What's the median age of the respondents? | 33.0 | number | ['What is your age? ๐ถ๐ป๐ต๐ป'] | ['number[uint8]'] | 32.5 |
What is the most common level of studies achieved? | Master | category | ['What is the maximum level of studies you have achieved? ๐'] | ['category'] | Master |
Which body complexity has the least number of respondents? | Very thin | category | ['What is your body complexity? ๐๏ธ'] | ['category'] | Obese |
What's the most frequent eye color? | Brown | category | ['What is your eye color? ๐๏ธ'] | ['category'] | Brown |
Which sexual orientation has the highest representation? | Heterosexual | category | [What's your sexual orientation?"]" | ['category'] | Heterosexual |
List the top 3 most common areas of knowledge. | ['[Computer Science]', '[Business]', '[Enginering & Architecture]'] | list[category] | ['What area of knowledge is closer to you?'] | ['list[category]'] | ['[Computer Science]', '[Business]', '[Enginering & Architecture]'] |
List the bottom 3 hair lengths in terms of frequency. | ['Medium', 'Long', 'Bald'] | list[category] | ['How long is your hair? ๐๐ปโ๏ธ๐๐ฝโ๏ธ'] | ['category'] | ['Short', 'Medium', 'Long'] |
Name the top 5 civil statuses represented in the dataset. | ['Single', 'Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Divorced'] | list[category] | ['What is your civil status? ๐'] | ['category'] | ['Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Single', 'Divorced'] |
๐พ๐๏ธ๐พ DataBench ๐พ๐๏ธ๐พ
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.
๐ 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 |
๐๏ธ 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.csv: 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.csv: sample containing 20 rows of the original dataset (DataBench lite)
- info.yml: additional information about the dataset
๐๏ธ 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 pandaโs 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 theyโre all UTC), as well as information about categoriesโ 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] |
๐ 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 Jorge Osรฉs Grijalba and Luis Alfonso Ureรฑa-Lรณpez and
Eugenio Martรญnez Cรกmara and Jose Camacho-Collados",
booktitle = "Proceedings of LREC-COLING 2024",
year = "2024",
address = "Turin, Italy"
}
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