MMTU-benchmark/MMTU huggingface dataset:
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| Round   | Václav Klaus   | Václav Klaus.1   | Jaroslava Moserová   | Jaroslava Moserová.1   | Miloš Zeman   | Miloš Zeman.1   |
|:--------|:---------------|:-----------------|:---------------------|:-----------------------|:--------------|:----------------|
| Round   | Deputies       | Senators         | Deputies             | Senators               | Deputies      | Senators        |
| 1st     | 89             | 32               | 25                   | 43                     | 78            | 5               |
| 1st     | 121            | 121              | 68                   | 68                     | 83            | 83              |
| 2nd     | 85             | 33               | 32                   | 42                     | -             | -               |
| 2nd     | 118            | 118              | 74                   | 74                     | -             | -               |
| 3rd     | 95             | 32               | 26                   | 39                     | -             | -               |
| 3rd     | 127            | 127              | 65                   | 65                     | -             | -               |

Question:
In the context of election data, a "majority win" is defined as a candidate receiving more than 50% of the total deputies and senators in a round. Based on the provided data, in which round did Miloš Zeman achieve his first majority win, if at all?


id: c8223578cf6dbfd367df3afbb270a180
qtype: NumericalReasoning
label: None
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   Year | Award                                      | Category              | Nominated work           | Result    | Ref.   |
|-------:|:-------------------------------------------|:----------------------|:-------------------------|:----------|:-------|
|   2015 | 30th Golden Rooster Awards                 | Best Supporting Actor | Dearest                  | Won       | -      |
|   2016 | 33rd Hundred Flowers Awards                | Best Supporting Actor | Dearest                  | Nominated | -      |
|   2017 | 24th Beijing College Student Film Festival | Best Actor            | Cock and Bull            | Won       | -      |
|   2017 | 8th China Film Director's Guild Awards     | Best Actor            | Cock and Bull            | Won       | -      |
|   2017 | 31st Golden Rooster Awards                 | Best Supporting Actor | Cock and Bull            | Nominated | -      |
|   2017 | 23rd Shanghai Television Festival          | Best Actor            | Feather Flies to the Sky | Won       | -      |
|   2018 | 31st Flying Apsaras Award                  | Outstanding Actor     | Feather Flies to the Sky | Nominated | -      |
|   2018 | 29th China TV Golden Eagle Award           | Best Actor            | Feather Flies to the Sky | Won       | -      |
|   2018 | 25th Beijing College Student Film Festival | Best Actor            | Operation Red Sea        | Nominated | -      |
|   2018 | 34th Hundred Flowers Awards                | Best Actor            | Operation Red Sea        | Nominated | -      |

Question:
Which film has the most award wins among those that were nominated for Best Actor and were released in 2017 or earlier?


id: f7ca4efbf662c36e751424be8b2f4d1f
qtype: FactChecking
label: Cock and Bull
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| engine type                        | scenario                     |   sfc in lb / (lbf h) |   sfc in g / (kn s) |   specific impulse (s) |   effective exhaust velocity (m / s) |
|:-----------------------------------|:-----------------------------|----------------------:|--------------------:|-----------------------:|-------------------------------------:|
| nk - 33 rocket engine              | vacuum                       |                10.9   |             309     |                    331 |                                 3240 |
| ssme rocket engine                 | space shuttle vacuum         |                 7.95  |             225     |                    453 |                                 4423 |
| ramjet                             | mach 1                       |                 4.5   |             127     |                    800 |                                 7877 |
| j - 58 turbojet                    | sr - 71 at mach 3.2 (wet)    |                 1.9   |              53.8   |                   1900 |                                18587 |
| rolls - royce / snecma olympus 593 | concorde mach 2 cruise (dry) |                 1.195 |              33.8   |                   3012 |                                29553 |
| cf6 - 80c2b1f turbofan             | boeing 747 - 400 cruise      |                 0.605 |              17.1   |                   5950 |                                58400 |
| general electric cf6 turbofan      | sea level                    |                 0.307 |               8.696 |                  11700 |                               115000 |

Question:
How many engines have a specific impulse greater than 1000 seconds?


id: b60f42005fcd5f0e80b5e791178df802
qtype: NumericalReasoning
label: 4
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| Month         | M36   |   M36B1 |   M36B2 |
|:--------------|:------|--------:|--------:|
| April 1944    | 25    |       0 |       0 |
| May 1944      | 100   |       0 |       0 |
| June 1944     | 120   |       0 |       0 |
| July 1944     | 155   |       0 |       0 |
| August 1944   | 100   |       0 |       0 |
| October 1944  | 75    |      50 |       0 |
| November 1944 | 290   |      93 |       0 |
| December 1944 | 348   |      44 |       0 |
| May 1945      | 10    |       0 |      50 |
| Post-war      | 190   |       0 |     674 |
| Total         | 1,413 |     187 |     724 |

Question:
What is the total number of medals (M36 + M36B1 + M36B2) earned from May 1944 to August 1944, and how does it compare to the total number of medals earned in the post-war period?


id: 50e30b3aec81d556db90cb2055da773b
qtype: NumericalReasoning
label: 475，less
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| index                         | organization                         |     year |   rank |   out of |
|:------------------------------|:-------------------------------------|---------:|-------:|---------:|
| bribe payers index            | transparency international           |     2011 |     19 |       28 |
| corruption perceptions index  | transparency international           |     2012 |     37 |      176 |
| democracy index               | economist intelligence unit          |     2010 |     36 |      167 |
| ease of doing business index  | world bank                           |     2012 |     16 |      185 |
| economic freedom index        | fraser institute                     |     2010 |     15 |      144 |
| economic freedom index        | the heritage foundation              |     2013 |     20 |      177 |
| global competitiveness report | world economic forum                 | 20122013 |     13 |      144 |
| global peace index            | institute for economics and peace    |     2011 |     27 |      153 |
| globalization index           | at kearney / foreign policy magazine |     2006 |     35 |       62 |
| press freedom index           | reporters without borders            |     2013 |     47 |      179 |
| property rights index         | property rights alliance             |     2008 |     28 |      115 |

Question:
In which year did the organization 'Transparency International' publish the highest-ranked index?


id: 412f39ec15ed20c84370029b2ff39ce5
qtype: NumericalReasoning
label: 2011
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   season | series                               | team                           |   races |   wins |   poles |   podiums |
|---------:|:-------------------------------------|:-------------------------------|--------:|-------:|--------:|----------:|
|     2006 | star of silverstone                  | silverstone motorsport academy |       2 |      1 |       0 |         2 |
|     2006 | formula renault 2.0 uk winter series | aka lemac                      |       4 |      0 |       0 |         0 |
|     2007 | eurocup formula renault 2.0          | sg drivers project             |      14 |      5 |       4 |         6 |
|     2007 | french formula renault 2.0           | sg formula                     |      10 |      2 |       1 |         3 |
|     2007 | formula renault 2.0 uk winter series | hitech junior team             |       4 |      0 |       0 |         0 |
|     2008 | formula 3 euro series                | art grand prix                 |      19 |      1 |       0 |         2 |
|     2008 | masters of formula 3                 | art grand prix                 |       1 |      0 |       0 |         1 |
|     2008 | macau grand prix                     | manor motorsport               |       1 |      0 |       0 |         0 |
|     2009 | formula renault 3.5 series           | comtec racing                  |      12 |      1 |       1 |         2 |
|     2010 | formula renault 3.5 series           | fortec motorsport              |      17 |      0 |       1 |         1 |
|     2011 | fia formula two championship         | motorsport vision              |       2 |      0 |       0 |         0 |
|     2011 | auto gp                              | super nova racing              |       4 |      1 |       0 |         1 |
|     2012 | gp2 series                           | ocean racing technology        |       2 |      0 |       0 |         0 |
|     2013 | gp2 series                           | hilmer motorsport              |       6 |      1 |       0 |         2 |

Question:
Which team did the driver participate with in the 2007 Eurocup Formula Renault 2.0 series?


id: 991bb7236167d5557333f1dc881d2110
qtype: FactChecking
label: sg drivers project
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| length (feet)      | year        | make and model         | floor type   |   number of seats |   bicycle capacity | fuel propulsion          |   quantity |
|:-------------------|:------------|:-----------------------|:-------------|------------------:|-------------------:|:-------------------------|-----------:|
| 30                 | 2001        | novabus rts            | high         |                27 |                  2 | diesel                   |          4 |
| 35                 | 2010        | new flyer de35lf       | low          |                29 |                  3 | diesel - electric hybrid |          7 |
| 40                 | 2000        | novabus rts            | high         |                39 |                  3 | diesel                   |         14 |
| 40                 | 2003        | orion bus industries v | high         |                41 |                  3 | diesel                   |         80 |
| 45                 | 1999        | mci 102dl3             | high         |                57 |                  2 | diesel                   |         14 |
| 45                 | 2003        | mci d4500              | high         |                57 |                  2 | diesel                   |          6 |
| 45                 | 2010 , 2012 | mci d4500ct            | high         |                57 |                  2 | diesel                   |         55 |
| 60 ( articulated ) | 2007        | new flyer d60lf        | low          |                58 |                  3 | diesel                   |         10 |

Question:
Which make and model of buses has the highest quantity?


id: 4f0c48014d19beeb80048111efe5b532
qtype: NumericalReasoning
label: Orion Bus Industries V
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   year (january) |   population (000) |   rural , % |   urban , % | source   |
|-----------------:|-------------------:|------------:|------------:|:---------|
|             1939 |               6081 |          72 |          28 | census   |
|             1959 |               9295 |          56 |          44 | census   |
|             1970 |              13001 |          50 |          50 | census   |
|             1979 |              14685 |          46 |          54 | census   |
|             1989 |              16537 |          43 |          57 | census   |
|             1999 |              14953 |          43 |          57 | census   |
|             2002 |              14851 |          43 |          57 | estimate |
|             2005 |              15075 |          43 |          57 | estimate |
|             2008 |              15572 |          47 |          53 | estimate |

Question:
Urbanization Rate is defined as the percentage of the population living in urban areas. Based on this definition, in which year did the urbanization rate surpass 50% for the first time?


id: 539b17209d5b8f10a98c96fe1029c2dc
qtype: NumericalReasoning
label: 1979
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   Unnamed: 0 | episode          | air date                  |   rating |   share | rating / share 1849   |   viewers (m) |   timeslot rank | night rank   |   overall rank |
|-------------:|:-----------------|:--------------------------|---------:|--------:|:----------------------|--------------:|----------------:|:-------------|---------------:|
|            1 | pilot            | tuesday , march 4 , 2008  |      8.2 |      12 | 4.5 / 11              |         13.47 |               1 | 2            |              6 |
|            2 | golden boy       | thursday , march 6 , 2008 |      6.2 |      10 | 3.5 / 8               |         10.12 |               2 | 4            |             15 |
|            3 | soldier 's heart | monday , march 10 , 2008  |      5.5 |       8 | 2.5 / 6               |          8.78 |               3 | 6            |             20 |
|            4 | honor            | monday , march 17 , 2008  |      4.5 |       7 | 2.3 / 6               |          7.3  |               4 | 10           |             37 |
|            5 | keep the change  | monday , march 24 , 2008  |      3.8 |       6 | 2.0 / 5               |          6.19 |               4 | 11           |             52 |
|            6 | legacy           | monday , march 31 , 2008  |      4.3 |       6 | 2.1 / 5               |          6.63 |               4 | 10           |             43 |
|            7 | reclassified     | monday , april 7 , 2008   |      4.6 |       7 | 2.2 / 5               |          7.44 |               4 | n / a        |             37 |

Question:
What is the average rating of episodes that have a viewership of at least 10 million and are ranked within the top 3 in their timeslot?


id: 0595618d0746974966d38bb82fc99faf
qtype: NumericalReasoning
label: 7.2
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   shirt number | player name           |   games played |   total points |   2 - points |   3 - points |   free throw |
|---------------:|:----------------------|---------------:|---------------:|-------------:|-------------:|-------------:|
|             11 | alpha ibrahim koroma  |              5 |             31 |           30 |            0 |            1 |
|             12 | alpha jalloh          |              5 |             17 |           16 |            0 |            1 |
|             13 | samuel juah           |              3 |              7 |            6 |            0 |            1 |
|             14 | abdulai bangura       |              2 |              4 |            4 |            0 |            0 |
|             15 | ibrahim jalloh        |              5 |              6 |            6 |            0 |            0 |
|             17 | ibrahim kallon        |              4 |              6 |            2 |            3 |            1 |
|             18 | ibrahim bakarr kamara |              3 |              2 |            2 |            0 |            0 |
|             22 | alpha ambrose kargbo  |              5 |             60 |           42 |           15 |            3 |
|             33 | amadu kargbo          |              4 |             14 |           12 |            0 |            2 |

Question:
How many 3-pointers did Alpha Ambrose Kargbo make in the given games?


id: d2db16edd343270475522cf1f70b86c6
qtype: FactChecking
label: 15
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| Color         |   Pin (Tip) |   Pin (Ring) | Color.1       |
|:--------------|------------:|-------------:|:--------------|
| White/Blue    |          26 |            1 | Blue/White    |
| White/Orange  |          27 |            2 | Orange/White  |
| White/Green   |          28 |            3 | Green/White   |
| White/Brown   |          29 |            4 | Brown/White   |
| White/Slate   |          30 |            5 | Slate/White   |
| Red/Blue      |          31 |            6 | Blue/Red      |
| Red/Orange    |          32 |            7 | Orange/Red    |
| Red/Green     |          33 |            8 | Green/Red     |
| Red/Brown     |          34 |            9 | Brown/Red     |
| Red/Slate     |          35 |           10 | Slate/Red     |
| Black/Blue    |          36 |           11 | Blue/Black    |
| Black/Orange  |          37 |           12 | Orange/Black  |
| Black/Green   |          38 |           13 | Green/Black   |
| Black/Brown   |          39 |           14 | Brown/Black   |
| Black/Slate   |          40 |           15 | Slate/Black   |
| Yellow/Blue   |          41 |           16 | Blue/Yellow   |
| Yellow/Orange |          42 |           17 | Orange/Yellow |
| Yellow/Green  |          43 |           18 | Green/Yellow  |
| Yellow/Brown  |          44 |           19 | Brown/Yellow  |
| Yellow/Slate  |          45 |           20 | Slate/Yellow  |
| Violet/Blue   |          46 |           21 | Blue/Violet   |
| Violet/Orange |          47 |           22 | Orange/Violet |
| Violet/Green  |          48 |           23 | Green/Violet  |
| Violet/Brown  |          49 |           24 | Brown/Violet  |
| Violet/Slate  |          50 |           25 | Slate/Violet  |

Question:
Which color combination has a higher 'Pin (Tip)' value, White/Blue or Red/Blue?


id: 1d88ca6fdff3b3e0089571e8c933e316
qtype: NumericalReasoning
label: Red/Blue
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| Province   |   DC |   LV | Ven.   |   PSI |   PDS |
|:-----------|-----:|-----:|:-------|------:|------:|
| Verona     | 34   | 20.6 | 6.6    |  11.1 |   7.1 |
| Vicenza    | 34.3 | 19.5 | 12.3   |   7.9 |   5.6 |
| Padua      | 34.5 | 14.8 | 8.1    |   9.3 |  10.5 |
| Treviso    | 32.9 | 21.5 | 7.8    |   9.7 |   7.5 |
| Belluno    | 27.4 | 27.8 | -      |  14.3 |   8   |
| Venice     | 23.8 | 13.4 | 6.6    |  13   |  16.2 |
| Rovigo     | 29.5 |  8.5 | 6.1    |  14.1 |  18.1 |
| Veneto     | 31.5 | 17.8 | 7.7    |  10.6 |   9.9 |

Question:
What is the difference in PSI values between the province with the highest PSI value and the province with the lowest PSI value?


id: 9294abdf58d2fa73160b9131f16ec61d
qtype: NumericalReasoning
label: 6.4
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   rank | nation         |   gold |   silver |   bronze |   total |
|-------:|:---------------|-------:|---------:|---------:|--------:|
|      1 | east germany   |     17 |        4 |        3 |      24 |
|      2 | austria        |     12 |       13 |       10 |      35 |
|      3 | russia         |     10 |        8 |        7 |      25 |
|      4 | great britain  |      6 |       11 |       11 |      28 |
|      5 | netherlands    |      6 |        3 |        3 |      12 |
|      6 | norway         |      6 |        0 |        0 |       6 |
|      7 | france         |      5 |        4 |        4 |      13 |
|      8 | italy          |      5 |        2 |        4 |      11 |
|      9 | germany        |      2 |        8 |        8 |      18 |
|     10 | czechoslovakia |      2 |        3 |        3 |       8 |
|     11 | switzerland    |      2 |        2 |        2 |       6 |
|     12 | canada         |      2 |        0 |        0 |       2 |
|     13 | finland        |      1 |        2 |        4 |       7 |
|     14 | hungary        |      1 |        2 |        3 |       6 |
|     15 | soviet union   |      0 |        7 |        6 |      13 |
|     16 | ukraine        |      0 |        3 |        3 |       6 |
|     17 | united states  |      0 |        1 |        1 |       2 |
|     18 | yugoslavia     |      0 |        1 |        0 |       1 |
|     19 | sweden         |      0 |        0 |        2 |       2 |
|     20 | georgia        |      0 |        0 |        2 |       2 |

Question:
What is the total number of medals won by switzerland ?


id: 6d38c7ec1308824230451156f31f1f00
qtype: FactChecking
label: 6
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| episode                      |   rating |   share |   viewers (millions) |   weekly rank |
|:-----------------------------|---------:|--------:|---------------------:|--------------:|
| slay everyone , trust no one |      8.1 |      13 |                14.15 |            14 |
| it 's getting the best of me |      6.9 |      11 |                11.94 |            11 |
| that girl is like a virus    |      6.7 |      10 |                11.6  |            14 |
| tonight , we make our move   |      7.2 |      12 |                12.72 |            17 |
| knights of the round table   |      7   |      11 |                12.17 |            14 |
| banana etiquette             |      6.6 |      11 |                11.15 |            15 |
| i'm not a good villain       |      6.7 |      12 |                11.26 |            11 |
| expectations                 |      7.3 |      12 |                12.38 |            13 |
| survivor history             |      7.1 |      13 |                12.31 |            12 |
| going down in flames         |      7   |      12 |                11.89 |             8 |
| jumping ship                 |      7.6 |      13 |                12.74 |             9 |
| a sinking ship               |      7.7 |      14 |                13.06 |             8 |
| loose lips sink ships        |      7.7 |      13 |                13.28 |            11 |
| anything could happen        |      7.2 |      12 |                13.46 |             9 |
| the reunion                  |      5.9 |      10 |                10.65 |            22 |

Question:
Which episode had the lowest rating?


id: 86e19cb374ce7c6940e9a9d467303067
qtype: NumericalReasoning
label: the reunion
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| crime               |   reported offenses |   killeen rate |   texas rate |   us rate |
|:--------------------|--------------------:|---------------:|-------------:|----------:|
| murder              |                  10 |            8.6 |          5.6 |       5.6 |
| rape                |                  66 |           56.9 |         32.9 |      29.4 |
| robbery             |                 216 |          186.4 |        155.2 |     154   |
| aggravated assault  |                 593 |          511.6 |        314.4 |     281.6 |
| violent crime       |                 885 |          763.5 |        508.2 |     470.6 |
| burglary            |                1711 |         1476.2 |        946.5 |     743.4 |
| larceny - theft     |                2877 |         2482.2 |       2688.9 |    2200.1 |
| motor vehicle theft |                 169 |          145.8 |        351.1 |     330.5 |
| non - violent crime |                4757 |         4104.2 |       3986.6 |    3274   |

Question:
How many types of violent crimes are reported in the table?


id: b08634d7cd884455337211051bbfc115
qtype: NumericalReasoning
label: 4
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| city / municipality   |   no of barangays |   area (km square) |   population (2010 census) |   pop density (per km square) |
|:----------------------|------------------:|-------------------:|---------------------------:|------------------------------:|
| angono                |                10 |              26.22 |                     102407 |                       3905.68 |
| antipolo              |                16 |             306.1  |                     677741 |                       2214.12 |
| baras                 |                10 |              84.93 |                      32609 |                        383.95 |
| binangonan            |                40 |              66.34 |                     249872 |                       3766.54 |
| cainta                |                 7 |              42.99 |                     311845 |                       7253.9  |
| cardona               |                18 |              28.56 |                      47414 |                       1660.15 |
| jalajala              |                11 |              44.12 |                      30074 |                        681.64 |
| morong                |                 8 |              37.58 |                      52194 |                       1388.88 |
| pililla               |                 9 |              69.95 |                      59527 |                        850.99 |
| rodriguez             |                11 |             312.7  |                     280904 |                        898.32 |
| san mateo             |                15 |              55.09 |                     205255 |                       3725.81 |
| tanay                 |                19 |             200    |                      98879 |                        494.3  |
| taytay                |                 5 |              38.8  |                     288956 |                       7447.32 |

Question:
Barangay Density is calculated as the number of barangays divided by the area in square kilometers. Which city/municipality has the highest barangay density?


id: 446d154f4598fc77bc4835e5b9c1b831
qtype: NumericalReasoning
label: cardona
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   year | competition   | location   | apparatus      |   rank - final |   score - final |   rank - qualifying |   score - qualifying |
|-------:|:--------------|:-----------|:---------------|---------------:|----------------:|--------------------:|---------------------:|
|   2011 | world cup     | ghent      | uneven bars    |              3 |          15.35  |                   1 |               15.35  |
|   2011 | world cup     | ghent      | balance beam   |              1 |          14.975 |                   2 |               14.85  |
|   2011 | world cup     | ghent      | floor exercise |              2 |          13.65  |                   3 |               13.475 |
|   2010 | world cup     | ghent      | uneven bars    |              1 |          15.05  |                   2 |               14.775 |
|   2010 | world cup     | ghent      | balance beam   |              3 |          13.65  |                   2 |               14.7   |
|   2010 | world cup     | ghent      | floor          |              6 |          12.7   |                   5 |               13.45  |
|   2010 | world cup     | doha       | uneven bars    |              2 |          13.85  |                   2 |               15.025 |
|   2010 | world cup     | doha       | balance beam   |              1 |          14.7   |                   1 |               14.525 |
|   2010 | world cup     | doha       | floor          |              1 |          13.975 |                   6 |               12.95  |

Question:
what is the difference of ghent's average final score from 2010 to 2011?


id: 8d2fb18ef60aa6895563b87a717d89e6
qtype: NumericalReasoning
label: 0.86
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| rank   | route                                      |   avg daily flts |   of airlines |   distance (km) | market share leader   |
|:-------|:-------------------------------------------|-----------------:|--------------:|----------------:|:----------------------|
| 1      | seoul ( gmp ) - jeju ( cju )               |              159 |             7 |             451 | korean air            |
| 2      | rio de janeiro ( sdu ) - são paulo ( cgh ) |              125 |             4 |             359 | tam                   |
| 3      | mumbai ( bom ) - new delhi ( del )         |              123 |             8 |            1138 | jet airways           |
| 4      | tokyo ( hnd ) - sapporo ( cts )            |              119 |             4 |             822 | all nippon airways    |
| 5      | monte carlo ( mcm ) - nice ( nce )         |              117 |             2 |              18 | heli air monaco       |
| 6t     | madrid ( mad ) - barcelona ( bcn )         |              116 |             4 |             484 | iberia                |
| 6t     | melbourne ( mel ) - sydney ( syd )         |              116 |             8 |             705 | qantas                |
| 8      | kahului ( ogg ) - honolulu ( hnl )         |               98 |             4 |             163 | hawaiian airlines     |
| 9t     | johannesburg ( jnb ) - cape town ( cpt )   |               92 |             6 |            1270 | south african airways |
| 9t     | fukuoka ( fuk ) - tokyo ( hnd )            |               92 |             3 |             881 | japan airlines        |

Question:
What is the total average daily flights of all routes combined?


id: 1aba704991b72a8107475d7fea800b42
qtype: NumericalReasoning
label: 1157
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
|   rank | english title           | chinese title   |   average |   peak |   premiere |   finale | hk viewers   |
|-------:|:------------------------|:----------------|----------:|-------:|-----------:|---------:|:-------------|
|      1 | the family link         | 師奶兵團        |        33 |     42 |         31 |       33 | 2.12 million |
|      2 | fathers and sons        | 爸爸閉翳        |        32 |     40 |         31 |       37 | 2.11 million |
|      3 | heart of greed          | 溏心風暴        |        32 |     48 |         29 |       40 | 2.08 million |
|      4 | ten brothers            | 十兄弟          |        32 |     39 |         29 |       36 | 2.05 million |
|      5 | on the first beat       | 學警出更        |        31 |     38 |         30 |       35 | 2.03 million |
|      6 | the green grass of home | 緣來自有機      |        31 |     36 |         29 |       33 | 2.01 million |
|      7 | dicey business          | 賭場風雲        |        31 |     37 |         30 |       34 | 1.99 million |
|      8 | steps                   | 舞動全城        |        31 |     36 |         31 |       32 | 1.98 million |
|      9 | the drive of life       | 歲月風雲        |        30 |     39 |         31 |       33 | 1.97 million |

Question:
What is the total average viewership of the top 5 TV shows?


id: 9c3b3255f540891cedc76da5e251d40f
qtype: NumericalReasoning
label: 160
Task: Table-QA (source dataset: TableBench)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| Province   |   DC |   LV* |   PSI |   PCI |
|:-----------|-----:|------:|------:|------:|
| Verona     | 44.3 |  10.8 |  14.2 |  11.5 |
| Vicenza    | 49.1 |  11.4 |  10.1 |   8.6 |
| Padua      | 46.1 |   6.4 |  10.7 |  16.3 |
| Treviso    | 44.5 |   7.8 |  14.1 |  12.1 |
| Belluno    | 39.3 |   7   |  23.8 |  13.1 |
| Venice     | 31.7 |   4.9 |  15.9 |  24.2 |
| Rovigo     | 35.2 |   3.3 |  15.5 |  29   |
| Veneto     | 42.3 |   7.8 |  13.7 |  15.5 |

Question:
What is the difference in DC values between the province of Verona and the province of Venice?


id: d494af84224f2aff206fa504b08926be
qtype: NumericalReasoning
label: 12.6