MMTU-benchmark/MMTU huggingface dataset:
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Home team       | Home team score   | Away team       | Away team score   | Venue                 |   Crowd | Date           |
|:----------------|:------------------|:----------------|:------------------|:----------------------|--------:|:---------------|
| richmond        | 16.9 (105)        | st kilda        | 16.14 (110)       | punt road oval        |   15000 | 18 august 1956 |
| fitzroy         | 9.16 (70)         | hawthorn        | 10.17 (77)        | brunswick street oval |    7500 | 18 august 1956 |
| essendon        | 14.13 (97)        | geelong         | 9.15 (69)         | windy hill            |   16500 | 18 august 1956 |
| collingwood     | 9.6 (60)          | melbourne       | 10.13 (73)        | victoria park         |   31089 | 18 august 1956 |
| carlton         | 6.9 (45)          | footscray       | 8.14 (62)         | princes park          |   44878 | 18 august 1956 |
| south melbourne | 13.9 (87)         | north melbourne | 7.13 (55)         | lake oval             |   10000 | 18 august 1956 |

Question: What is the lowest attendance when footscray is the away team?

table_id: 2-10640687-18
label: SELECT MIN("Crowd") FROM "table" WHERE "Away team" = "footscray"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Player          |   Touchdowns |   Extra points |   Field goals |   Points |
|:----------------|-------------:|---------------:|--------------:|---------:|
| tom hammond     |           15 |             63 |             5 |      163 |
| herb graver     |           15 |              1 |             0 |       76 |
| willie heston   |           14 |              0 |             0 |       70 |
| joe curtis      |            8 |             13 |             0 |       53 |
| joe maddock     |           10 |              0 |             0 |       50 |
| duncan thompson |           10 |              0 |             0 |       50 |
| fred norcross   |            9 |              0 |             0 |       45 |

Question: What is the highest number of extra points?

table_id: 1-14342480-15
label: SELECT MAX("Extra points") FROM "table"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Title                                          | Label                  |   Year of Release | Country of Release   | Peaches:     |
|:-----------------------------------------------|:-----------------------|------------------:|:---------------------|:-------------|
| shake your groove thing                        | mvp / polydor          |              1978 | usa                  | linda greene |
| roller skatin' mate                            | mvp / polydor          |              1979 | usa                  | linda greene |
| fun time                                       | mvp / polydor          |              1980 | usa                  | linda greene |
| freeway                                        | mvp / polydor          |              1981 | usa                  | linda greene |
| shake your groove thing (serious rope remixes) | mother / mvp / polydor |              1994 | uk                   | linda greene |

Question: What was the title for the label mother / mvp / polydor?

table_id: 2-1862179-4
label: SELECT "Title" FROM "table" WHERE "Label" = "mother / mvp / polydor"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Call sign   | Frequency MHz   | City of license         | ERP W         | FCC info                                        |
|:------------|:----------------|:------------------------|:--------------|:------------------------------------------------|
| k280ef      | 88.9 fm         | austin, minnesota       | 9 watts       | fcc                                             |
| knsr        | 88.9 fm hd2     | collegeville, minnesota | 100,000 watts | [http://www.fcc.gov/fcc-bin/fmq?call= knsr fcc] |
| w248as      | 97.5 fm         | hinckley, minnesota     | 55 watts      | fcc                                             |
| k286aw      | 105.1 fm        | mankato, minnesota      | 10 watts      | fcc                                             |
| k237et      | 95.3 fm         | new ulm, minnesota      | 250 watts     | fcc                                             |
| kcmp        | 89.3 fm         | northfield, minnesota   | 100,000 watts | fcc                                             |
| kpcc        | 89.3 fm hd3     | pasadena, california    | 600 watts     | [http://www.fcc.gov/fcc-bin/fmq?call= kpcc fcc] |
| kmse        | 88.7 fm         | rochester, minnesota    | 850 watts     | [http://www.fcc.gov/fcc-bin/fmq?call= kmse fcc] |
| k228xn      | 93.5 fm         | st. peter, minnesota    | 60 watts      | fcc                                             |

Question: Name the city of license for call sign of kcmp

table_id: 2-1191667-1
label: SELECT "City of license" FROM "table" WHERE "Call sign" = "kcmp"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
|   Season # |   Series # | Title                | Canadian airdate   | U.S. airdate   |   Production code |
|-----------:|-----------:|:---------------------|:-------------------|:---------------|------------------:|
|          1 |         14 | "dangerous liaisons" | april 21, 2009     | april 3, 2009  |               201 |
|          2 |         15 | "documentary"        | april 27, 2009     | april 10, 2009 |               202 |
|          3 |         16 | "dermabrasion"       | may 4, 2009        | april 17, 2009 |               203 |
|          4 |         17 | "different hearts"   | may 11, 2009       | april 24, 2009 |               204 |
|          5 |         18 | "destiny"            | may 18, 2009       | may 1, 2009    |               205 |
|          6 |         19 | "duluth, minnesota"  | may 25, 2009       | may 8, 2009    |               206 |
|          7 |         20 | "debtor's prison"    | june 1, 2009       | may 15, 2009   |               207 |

Question: what is the minimum production code

table_id: 1-12294557-3
label: SELECT MIN("Production code") FROM "table"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Delegate     | Interview   | Swimsuit   | Evening Gown   | Average    |
|:-------------|:------------|:-----------|:---------------|:-----------|
| usa          | 8.155 (1)   | 7.855 (1)  | 7.482 (2)      | 7.831 (1)  |
| sweden       | 7.518 (2)   | 7.300 (2)  | 7.355 (3)      | 7.391 (2)  |
| spain        | 6.500 (5)   | 6.870 (3)  | 7.545 (1)      | 6.972 (3)  |
| south africa | 6.791 (3)   | 6.590 (4)  | 6.373 (5)      | 6.685 (4)  |
| colombia     | 6.645 (4)   | 6.282 (6)  | 6.545 (4)      | 6.491 (5)  |
| belgium      | 6.230 (6)   | 6.364 (5)  | 5.709 (6)      | 6.101 (6)  |
| chile        | 5.703 (7)   | 5.064 (7)  | 4.536 (9)      | 4.891 (7)  |
| israel       | 5.260 (9)   | 4.280 (8)  | 4.945 (7)      | 4.828 (8)  |
| mexico       | 5.320 (8)   | 3.260 (11) | 4.145 (10)     | 4.242 (9)  |
| ireland      | 3.889 (12)  | 3.800 (9)  | 4.756 (8)      | 4.148 (10) |
| peru         | 4.422 (10)  | 3.718 (10) | 3.573 (11)     | 3.904 (11) |

Question: Name the average for spain delegate

table_id: 1-11674683-2
label: SELECT "Average" FROM "table" WHERE "Delegate" = "Spain"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Place   | Player             | Country                | Score           |   To par | Money ( $ )   |
|:--------|:-------------------|:-----------------------|:----------------|---------:|:--------------|
| t1      | billy burke        | united states          | 73-72-74-73=292 |        8 | playoff       |
| t1      | george von elm     | united states          | 75-69-73-75=292 |        8 | playoff       |
| 3       | leo diegel         | united states          | 75-73-74-72=294 |       10 | 650           |
| t4      | wiffy cox          | united states          | 76-74-74-72=296 |       12 | 450           |
| t4      | bill mehlhorn      | united states          | 77-73-75-71=296 |       12 | 450           |
| t4      | gene sarazen       | united states          | 74-78-74-70=296 |       12 | 450           |
| t7      | mortie dutra       | united states          | 71-77-73-76=297 |       13 | 200           |
| t7      | walter hagen       | united states          | 74-74-73-76=297 |       13 | 200           |
| t7      | philip perkins (a) | england                | 78-76-73-70=297 |       13 | 0             |
| t10     | al espinosa        | united states          | 72-78-75-74=299 |       15 | 105           |
| t10     | johnny farrell     | united states          | 78-70-79-72=299 |       15 | 105           |
| t10     | macdonald smith    | scotland united states | 73-73-75-78=299 |       15 | 105           |

Question: Which player has $450 and a score of 76-74-74-72=296?

table_id: 2-18007126-1
label: SELECT "Player" FROM "table" WHERE "Money ( $ )" = "450" AND "Score" = "76-74-74-72=296"
Task: NL2SQL (source dataset: WikiSQL)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Table:
| Rank   | Nation         |   Gold |   Silver |   Bronze |   Total |
|:-------|:---------------|-------:|---------:|---------:|--------:|
| 1      | east germany   |     15 |       11 |        3 |      29 |
| 2      | soviet union   |     13 |       11 |        8 |      32 |
| 3      | great britain  |      3 |        2 |        3 |       8 |
| 4      | west germany   |      2 |        2 |        4 |       8 |
| 5      | hungary        |      2 |        0 |        1 |       3 |
| 6      | sweden         |      1 |        4 |        2 |       7 |
| 7      | yugoslavia     |      1 |        1 |        1 |       3 |
| 8      | netherlands    |      0 |        3 |        4 |       7 |
| 9      | poland         |      0 |        1 |        3 |       4 |
| 10     | austria        |      0 |        1 |        2 |       3 |
| 11     | italy          |      0 |        1 |        1 |       2 |
| 12     | spain          |      0 |        0 |        1 |       1 |
| 12     | czechoslovakia |      0 |        0 |        1 |       1 |
| 12     | france         |      0 |        0 |        1 |       1 |
| 12     | romania        |      0 |        0 |        1 |       1 |
| 12     | switzerland    |      0 |        0 |        1 |       1 |
| total  | total          |     37 |       37 |       37 |     111 |

Question: How many total medals for the nation ranked 7 with 1 silver and under 4 bronzes?

table_id: 2-13038386-1
label: SELECT AVG("Total") FROM "table" WHERE "Bronze" < 4 AND "Silver" = 1 AND "Rank" = "7"
Task: Table-QA (source dataset: FinQA)

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                        | ( in millions )   |
|:----------------------------------|:------------------|
| for the years ending december 31, |                   |
| 2018                              | $ 64              |
| 2019                              | 54                |
| 2020                              | 52                |
| 2021                              | 19                |
| 2022                              | 16                |

Question:
what was the average amortization expense between 2015 and 2017


pre_text: humana inc .
notes to consolidated financial statements 2014 ( continued ) amortization expense for other intangible assets was approximately $ 75 million in 2017 , $ 77 million in 2016 , and $ 93 million in 2015 .
the following table presents our estimate of amortization expense for each of the five next succeeding fiscal years: .
post_text: .
label: 81.66667
qa: answer: 81.6
ann_text_rows: 1
steps: op: add1-1
arg1: 75
arg2: 77
res: 152
op: add1-2
arg1: #0
arg2: 93
res: 245
op: divide1-3
arg1: #1
arg2: const_3
res: 81.6
program: add(75, 77), add(#0, 93), divide(#1, const_3)
gold_inds: text_1: notes to consolidated financial statements 2014 ( continued ) amortization expense for other intangible assets was approximately $ 75 million in 2017 , $ 77 million in 2016 , and $ 93 million in 2015 .
exe_ans: 81.66667
tfidftopn: text_2: the following table presents our estimate of amortization expense for each of the five next succeeding fiscal years: .
text_0: humana inc .
program_re: divide(add(add(75, 77), 93), const_3)
model_input: text_1
notes to consolidated financial statements 2014 ( continued ) amortization expense for other intangible assets was approximately $ 75 million in 2017 , $ 77 million in 2016 , and $ 93 million in 2015 .
text_2
the following table presents our estimate of amortization expense for each of the five next succeeding fiscal years: .
table_2
the 2018 of ( in millions ) is $ 64 ;
Task: Table-QA (source dataset: FinQA)

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:
| balance at january 1 2005    | $ 2014       |
|:-----------------------------|:-------------|
| expensed in 2005             | 141          |
| cash payments in 2005        | -23 ( 23 )   |
| foreign currency revaluation | -2 ( 2 )     |
| balance at december 31 2005  | 116          |
| expensed in 2006             | 155          |
| cash payments in 2006        | -141 ( 141 ) |
| foreign currency revaluation | 4            |
| balance at december 31 2006  | 134          |
| expensed in 2007             | 38           |
| cash payments in 2007        | -110 ( 110 ) |
| foreign currency revaluation | 1            |
| balance at december 31 2007  | $ 63         |

Question:
what is the net change in aon 2019s unpaid restructuring liabilities during 2006?


pre_text: notes to consolidated financial statements the following table sets forth the activity related to the 2005 restructuring plan liabilities .
( millions ) .
post_text: aon 2019s unpaid restructuring liabilities are included in both accounts payable and accrued liabilities and other non-current liabilities in the consolidated statements of financial position .
aon corporation .
label: 18
qa: answer: 18
ann_table_rows: 5
6
7
steps: op: add2-1
arg1: 155
arg2: -141
res: 14
op: add2-2
arg1: #0
arg2: 4
res: 18
program: add(155, -141), add(#0, 4)
gold_inds: table_5: balance at january 1 2005 the expensed in 2006 of $ 2014 is 155 ;
table_6: balance at january 1 2005 the cash payments in 2006 of $ 2014 is -141 ( 141 ) ;
table_7: balance at january 1 2005 the foreign currency revaluation of $ 2014 is 4 ;
exe_ans: 18
program_re: add(add(155, -141), 4)
model_input: table_5
balance at january 1 2005 the expensed in 2006 of $ 2014 is 155 ;
table_8
balance at january 1 2005 the balance at december 31 2006 of $ 2014 is 134 ;
table_12
balance at january 1 2005 the balance at december 31 2007 of $ 2014 is $ 63 ;
Task: Table-QA (source dataset: FinQA)

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:
| balance at january 1 2005    | $ 2014       |
|:-----------------------------|:-------------|
| expensed in 2005             | 141          |
| cash payments in 2005        | -23 ( 23 )   |
| foreign currency revaluation | -2 ( 2 )     |
| balance at december 31 2005  | 116          |
| expensed in 2006             | 155          |
| cash payments in 2006        | -141 ( 141 ) |
| foreign currency revaluation | 4            |
| balance at december 31 2006  | 134          |
| expensed in 2007             | 38           |
| cash payments in 2007        | -110 ( 110 ) |
| foreign currency revaluation | 1            |
| balance at december 31 2007  | $ 63         |

Question:
what is the net change in aon 2019s unpaid restructuring liabilities during 2007?


pre_text: notes to consolidated financial statements the following table sets forth the activity related to the 2005 restructuring plan liabilities .
( millions ) .
post_text: aon 2019s unpaid restructuring liabilities are included in both accounts payable and accrued liabilities and other non-current liabilities in the consolidated statements of financial position .
aon corporation .
label: -71
qa: answer: -71
ann_table_rows: 9
10
11
steps: op: add1-1
arg1: 38
arg2: -110
res: -72
op: add1-2
arg1: #0
arg2: 1
res: -71
program: add(38, -110), add(#0, 1)
gold_inds: table_9: balance at january 1 2005 the expensed in 2007 of $ 2014 is 38 ;
table_10: balance at january 1 2005 the cash payments in 2007 of $ 2014 is -110 ( 110 ) ;
table_11: balance at january 1 2005 the foreign currency revaluation of $ 2014 is 1 ;
exe_ans: -71
program_re: add(add(38, -110), 1)
model_input: table_4
balance at january 1 2005 the balance at december 31 2005 of $ 2014 is 116 ;
table_9
balance at january 1 2005 the expensed in 2007 of $ 2014 is 38 ;
table_12
balance at january 1 2005 the balance at december 31 2007 of $ 2014 is $ 63 ;
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 |   property taxes |   investment earnings |   other local sources |   state & federal |   total revenue |
|-------:|-----------------:|----------------------:|----------------------:|------------------:|----------------:|
|   2005 |      2.43849e+07 |      255041           |           2.67006e+06 |       1.3582e+07  |     4.08917e+07 |
|   2004 |      2.10992e+07 |      181103           |           2.62413e+06 |       1.39992e+07 |     3.79036e+07 |
|   2003 |      1.71992e+07 |      509862           |           2.30909e+06 |       1.27949e+07 |     3.25396e+07 |
|   2002 |      1.43592e+07 |      879398           |           2.1681e+06  |       1.51329e+07 |     3.25396e+07 |
|   2001 |      1.16312e+07 |           1.94988e+06 |           1.98744e+06 |       1.29295e+07 |     2.8498e+07  |
|   2000 |      1.06087e+07 |      493839           |           2.12748e+06 |       8.54956e+06 |     2.17796e+07 |

Question:
In the context of local government finance, "Revenue Growth Rate" is defined as the percentage change in total revenue from one year to the next. What was the revenue growth rate from 2001 to 2002, based on the provided data?


id: 106f3d501745fe92f5e6b513acff4a61
qtype: NumericalReasoning
label: 14.18
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 | united states |     50 |       41 |       32 |     123 |
|      2 | great britain |     18 |       23 |       20 |      61 |
|      3 | italy         |     14 |       15 |       16 |      45 |
|      4 | australia     |     12 |       11 |        7 |      30 |
|      5 | rhodesia      |     10 |        5 |        2 |      17 |
|      6 | south africa  |      8 |        8 |        3 |      19 |
|      7 | israel        |      7 |        3 |       11 |      21 |
|      8 | argentina     |      6 |       15 |       16 |      37 |
|      9 | west germany  |      5 |        2 |        5 |      12 |
|     10 | netherlands   |      4 |        6 |        4 |      14 |

Question:
According to the table, which nation won 18 gold medals?


id: 3e8c9ae6880c285746e8344707583b81
qtype: FactChecking
label: great britain
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   |   indians admitted |   pakistanis admitted |   sri lankans admitted |   bangladeshis admitted |   nepalis admitted |
|:-------|-------------------:|----------------------:|-----------------------:|------------------------:|-------------------:|
| 2000   |              26122 |                 14201 |                   5849 |                    2715 |                247 |
| 2001   |              27901 |                 15353 |                   5520 |                    3393 |                273 |
| 2002   |              28838 |                 14173 |                   4968 |                    2615 |                418 |
| 2003   |              24595 |                 12351 |                   4448 |                    1896 |                440 |
| 2004   |              25573 |                 12793 |                   4134 |                    3374 |                594 |
| 2005   |              22141 |                 13575 |                   4690 |                    3940 |                714 |
| 2006   |              30746 |                 12329 |                   4490 |                    3838 |                640 |
| 2007   |              26047 |                  9545 |                   3934 |                    2735 |                564 |
| 2008   |              24548 |                  8051 |                   4508 |                    2716 |                639 |
| 2009   |              26117 |                  6213 |                   4270 |                    4270 |                627 |
| 2010   |              30252 |                  4986 |                   4181 |                    4364 |               1502 |
| 2011   |              24965 |                  6073 |                   3104 |                    2449 |               1249 |
| 2012   |              28943 |                  9931 |                   3152 |                    2449 |               1311 |
| total  |             346788 |                139574 |                  57248 |                   35856 |               9218 |

Question:
How many Indians were admitted in 2005?


id: 993a7fd34ef053762ab118cd5ae0a3c0
qtype: FactChecking
label: 22141
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:
|   position | team                          |   points |   played |   drawn |   lost |   against | difference   |
|-----------:|:------------------------------|---------:|---------:|--------:|-------:|----------:|:-------------|
|          1 | palestra itália - sp          |       18 |        9 |       0 |      0 |         8 | 25           |
|          2 | auto                          |       14 |        9 |       0 |      2 |        14 | 11           |
|          3 | corinthians                   |       13 |        9 |       1 |      2 |         9 | 17           |
|          4 | santos                        |       11 |        9 |       1 |      3 |        17 | 7            |
|          5 | sílex                         |        8 |        9 |       2 |      4 |        26 | - 9          |
|          6 | sírio                         |        7 |        9 |       1 |      5 |        20 | 1            |
|          7 | ypiranga - sp                 |        7 |        9 |       1 |      5 |        22 | - 6          |
|          8 | portuguesa                    |        7 |        9 |       3 |      4 |        26 | - 9          |
|          9 | aa são bento                  |        4 |        9 |       0 |      7 |        31 | - 22         |
|         10 | sc internacional de são paulo |        1 |        9 |       1 |      8 |        24 | - 15         |

Question:
Which team is currently with 13 points and a goal difference of 17?


id: 9a37bff1bac90eb54cc51e99d4abef15
qtype: FactChecking
label: corinthians
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:
| detailed family information                         |   from |   to |   anchor | orientation   | conserved in mus musculus   |   matrix sim | sequence                  |   occurrence |
|:----------------------------------------------------|-------:|-----:|---------:|:--------------|:----------------------------|-------------:|:--------------------------|-------------:|
| cell cycle regulators : cell cycle homology element |    137 |  149 |      143 | + strand      | conserved                   |        0.943 | ggacttgaattca             |            1 |
| gata binding factors                                |    172 |  184 |      178 | + strand      | conserved                   |        0.946 | taaagatttgagg             |            1 |
| vertebrate tata binding protein factor              |    193 |  209 |      201 | + strand      | conserved                   |        0.983 | tcctataaaatttggat         |            1 |
| heat schock factors                                 |    291 |  315 |      303 | + strand      | conserved                   |        0.992 | cacagaaacgttagaagcatctctt |            4 |
| human and murine ets1 factors                       |    512 |  532 |      522 | + strand      | conserved                   |        0.984 | taagccccggaagtacttgtt     |            3 |
| zinc finger transcription factor ru49 , zipro1      |    522 |  528 |      525 | + strand      | conserved                   |        0.989 | aagtact                   |            2 |
| krueppel like transcription factors                 |    618 |  634 |      626 | + strand      | conserved                   |        0.925 | tggaggggcagacaccc         |            1 |

Question:
How many transcription factors have an occurrence count greater than 1?


id: 663af775c49891f680893517237e7158
qtype: NumericalReasoning
label: 3
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 |   number of tropical storms |   number of hurricanes |   number of major hurricanes | deaths   | strongest storm          |
|-------:|----------------------------:|-----------------------:|-----------------------------:|:---------|:-------------------------|
|   1860 |                           1 |                      5 |                            1 | 60 +     | one                      |
|   1861 |                           2 |                      6 |                            0 | 22 +     | one and three            |
|   1862 |                           3 |                      3 |                            0 | 3        | two and three            |
|   1863 |                           4 |                      5 |                            0 | 90       | one , two , three & four |
|   1864 |                           2 |                      3 |                            0 | none     | one , three & five       |
|   1865 |                           4 |                      3 |                            0 | 326      | four & seven             |
|   1866 |                           1 |                      5 |                            1 | 383      | six                      |
|   1867 |                           2 |                      6 |                            0 | 811      | 'san narciso'            |
|   1868 |                           1 |                      3 |                            0 | 2        | one , two & four         |

Question:
In which year did the number of tropical storms increase the most compared to the previous year?


id: ea5ef854f4eee8168e738c24f77b6a19
qtype: NumericalReasoning
label: 1865
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      | south korea    |     11 |        1 |        1 |      13 |
| 2      | spain          |      3 |        1 |        2 |       6 |
| 3      | chinese taipei |      1 |        1 |        2 |       4 |
| 4      | france         |      1 |        0 |        1 |       2 |
| 5      | turkey         |      0 |        2 |        3 |       5 |
| 6      | mexico         |      0 |        2 |        2 |       4 |
| 7      | canada         |      0 |        2 |        1 |       3 |
| 7      | greece         |      0 |        2 |        1 |       3 |
| 9      | brazil         |      0 |        2 |        0 |       2 |
| 10     | venezuela      |      0 |        1 |        1 |       2 |
| 11     | denmark        |      0 |        1 |        0 |       1 |
| 11     | indonesia      |      0 |        1 |        0 |       1 |
| 13     | united states  |      0 |        0 |        4 |       4 |
| 14     | egypt          |      0 |        0 |        2 |       2 |
| 14     | germany        |      0 |        0 |        2 |       2 |
| 16     | argentina      |      0 |        0 |        1 |       1 |
| 16     | colombia       |      0 |        0 |        1 |       1 |
| 16     | cyprus         |      0 |        0 |        1 |       1 |
| 16     | finland        |      0 |        0 |        1 |       1 |
| 16     | malaysia       |      0 |        0 |        1 |       1 |
| 16     | netherlands    |      0 |        0 |        1 |       1 |
| 16     | nigeria        |      0 |        0 |        1 |       1 |
| 16     | philippines    |      0 |        0 |        1 |       1 |
| 16     | puerto rico    |      0 |        0 |        1 |       1 |
| 16     | sweden         |      0 |        0 |        1 |       1 |
| total  | total          |     16 |       16 |       32 |      64 |

Question:
Which nation has a total of 13 medals ?


id: 78f1a1ec29ac20f339fbc3fb396b02d1
qtype: FactChecking
label: south korea