MMTU-benchmark/MMTU huggingface dataset:
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|       Col_0 | Col_1        |   Col_2 | Col_3      | Col_4   |   Col_5 | Col_6    | Col_7                      | Col_8   | Col_9   | Col_10   |
|------------:|:-------------|--------:|:-----------|:--------|--------:|:---------|:---------------------------|:--------|:--------|:---------|
| 2.32906e+06 | prd922329062 |    1117 | MBhargavan | F       |      30 |          |                            |         | Portal  | Open     |
| 2.32906e+06 | prd922329062 |    1117 | MBhargavan | F       |      30 |          |                            |         | Portal  | Open     |
| 2.33888e+06 | prd922338878 |    1228 | jburleson  | F       |      30 | 114137.0 | US_ duplex doppler_ pelvis | 9.0     | Portal  | Closed   |
| 2.32906e+06 | prd922329062 |    1117 | MBhargavan | F       |      30 |          |                            |         | Portal  | Open     |
| 2.33888e+06 | prd922338878 |    1228 | jburleson  | F       |      30 | 114137.0 | US_ duplex doppler_ pelvis | 9.0     | Portal  | Closed   |

Candidate column headers:
UserID
DecisionSupportID
PatientGender
PatientAge
DataMode
UserName
FinalDSScore
DSSessionID
Status
RulesSPVID
ProcedureText



headers: UserID
DecisionSupportID
PatientGender
PatientAge
DataMode
UserName
FinalDSScore
DSSessionID
Status
RulesSPVID
ProcedureText
label: DSSessionID
DecisionSupportID
UserID
UserName
PatientGender
PatientAge
RulesSPVID
ProcedureText
FinalDSScore
DataMode
Status
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1          | Col_2   | Col_3     | Col_4              |   Col_5 |
|--------:|:---------------|:--------|:----------|:-------------------|--------:|
|      42 | South Dakota   | SD      | Midwest   | West North Central |       4 |
|      38 | Oregon         | OR      | West      | Pacific            |       9 |
|      10 | Florida        | FL      | South     | South Atlantic     |       5 |
|      28 | North Carolina | NC      | South     | South Atlantic     |       5 |
|      39 | Pennsylvania   | PA      | Northeast | Middle Atlantic    |       2 |

Candidate column headers:
StateDivisionName
StateDivisionNumber
StateID
StateRegion
State
StateAbbreviation



headers: StateDivisionName
StateDivisionNumber
StateID
StateRegion
State
StateAbbreviation
label: StateID
State
StateAbbreviation
StateRegion
StateDivisionName
StateDivisionNumber
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0      | Col_1       | Col_2        | Col_3   | Col_4           | Col_5        | Col_6   | Col_7   |   Col_8 | Col_9     | Col_10   | Col_11                      | Col_12             | Col_13             |
|:-----------|:------------|:-------------|:--------|:----------------|:-------------|:--------|:--------|--------:|:----------|:---------|:----------------------------|:-------------------|:-------------------|
| Farm Scale | Modesto     | Stanislaus   | CA      | Complete Mix    | Operational  | 2008.0  | Dairy   |    2513 | 165000.0  | 710.0    | Storage Tank or Pond or Pit | 254.4153633497748  | 5342.722630345271  |
| Farm Scale | Trenton     | Gilchrist    | FL      | Mixed Plug Flow | Operational  | 2012.0  | Dairy   |    5000 |           | 1000.0   | Storage Tank or Pond or Pit | 800.969009345833   | 16820.34919626249  |
| Farm Scale | Canton      | St. Lawrence | NY      | Complete Mix    | Construction | 2014.0  | Dairy   |    2300 | 122800.0  | 400.0    | Storage Tank or Pond or Pit |                    |                    |
| Farm Scale | Morrilton   | Conway       | AR      | Covered Lagoon  | Operational  | 2008.0  | Swine   |    4200 | 1814400.0 |          | Storage Lagoon              | 135.58498389209498 | 2847.2846617339947 |
|            | Sun Prairie | Dane         | WI      | Mixed Plug Flow | Construction |         | Dairy   |    2500 |           | 600.0    | Storage Tank or Pond or Pit |                    |                    |

Candidate column headers:
Project_type
State
Population Feeding Digester
Animal Type
City
Biogas Generation Estimate (cu_ft/day)
Year Operational
Digester Type
Status
County
Baseline System
Installed Capacity (kW)
Methane Emission Reductions (metric tons CO2E/yr)
Methane Emission Reductions (metric tons CH4/yr)



headers: Project_type
State
Population Feeding Digester
Animal Type
City
Biogas Generation Estimate (cu_ft/day)
Year Operational
Digester Type
Status
County
Baseline System
Installed Capacity (kW)
Methane Emission Reductions (metric tons CO2E/yr)
Methane Emission Reductions (metric tons CH4/yr)
label: Project_type
City
County
State
Digester Type
Status
Year Operational
Animal Type
Population Feeding Digester
Biogas Generation Estimate (cu_ft/day)
Installed Capacity (kW)
Baseline System
Methane Emission Reductions (metric tons CH4/yr)
Methane Emission Reductions (metric tons CO2E/yr)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1          |       Col_2 |            Col_3 |       Col_4 |            Col_5 |       Col_6 |       Col_7 |       Col_8 |       Col_9 |
|--------:|:---------------|------------:|-----------------:|------------:|-----------------:|------------:|------------:|------------:|------------:|
|     508 | Chicago        | 1.00315e+08 |      1.7274e+07  | 5.38493e+07 |      3.20466e+06 | 8.00825e+07 | 9.91779e+07 | 1.834e+07   | 3.72243e+08 |
|     510 | South Suburban | 1.38104e+07 | 607134           | 6.93749e+06 | 786856           | 1.10425e+07 | 1.2072e+07  | 2.48012e+06 | 4.77365e+07 |
|     512 | Harper         | 5.6766e+07  |      1.09612e+06 | 8.8745e+06  |      3.00655e+06 | 1.5478e+07  | 4.98996e+07 | 4.00906e+06 | 1.3913e+08  |
|     504 | Triton         | 2.77619e+07 | 260061           | 3.20654e+06 | 316595           | 1.3322e+07  | 2.75168e+07 | 1.93999e+06 | 7.43239e+07 |
|     539 | John Wood      | 5.81861e+06 |  10000           | 2.06334e+06 |  77968           | 5.17873e+06 | 7.71941e+06 | 4.28886e+06 | 2.51569e+07 |

Candidate column headers:
Other  Local  Revenue
Total
Local  Tax  Revenue
Other  Misc.  Revenue
District
Federal  Revenue
    Tuition  and  Fees    
ICCB  Grants
District  Number
Other  State Revenue



headers: Other  Local  Revenue
Total
Local  Tax  Revenue
Other  Misc.  Revenue
District
Federal  Revenue
    Tuition  and  Fees    
ICCB  Grants
District  Number
Other  State Revenue
label: District  Number
District
Local  Tax  Revenue
Other  Local  Revenue
ICCB  Grants
Other  State Revenue
Federal  Revenue
    Tuition  and  Fees    
Other  Misc.  Revenue
Total
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                          | Col_1   | Col_2   | Col_3   | Col_4   |   Col_5 | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   |
|:-------------------------------|:--------|:--------|:--------|:--------|--------:|:--------|:--------|:--------|:--------|:---------|
| Business: Genl Office Skill AA | 2.0     | 2.0     | 5.0     | 1.0     |       2 | 1.0     | 2.0     | 1.0     | 2.0     | 1.0      |
| CD-School Age Child Care CC    |         |         |         |         |       1 |         |         |         |         |          |
| Award Career Prep              |         |         | 3.0     |         |       5 | 7.0     | 2.0     |         |         |          |
| Administration of Justice AS-T |         |         |         | 6.0     |      30 | 31.0    | 28.0    | 41.0    | 33.0    | 45.0     |
| CD: Early Childhood Educ CA    | 5.0     | 4.0     | 3.0     | 10.0    |      13 | 7.0     | 13.0    | 5.0     | 16.0    | 8.0      |

Candidate column headers:
2016-2017
Program
2011-2012
2013-2014
2010-2011
2012-2013
2014-2015
2009-2010
2008-2009
2017-2018
2015-2016



headers: 2016-2017
Program
2011-2012
2013-2014
2010-2011
2012-2013
2014-2015
2009-2010
2008-2009
2017-2018
2015-2016
label: Program
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
2014-2015
2015-2016
2016-2017
2017-2018
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   | Col_3   |   Col_4 |
|--------:|:--------|:--------|:--------|--------:|
|   41641 | S       | M       | P3      |     238 |
|   41640 | S       | M       | P3      |     128 |
|   41640 | V       | M       | P2      |     215 |
|   41641 | S       | O       | P3      |     235 |
|   41642 | S       | O       | P1      |     189 |

Candidate column headers:
site
produit
Date
Pack
qte



headers: site
produit
Date
Pack
qte
label: Date
Pack
site
produit
qte
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0             |   Col_1 |   Col_2 |   Col_3 |
|:------------------|--------:|--------:|--------:|
| 978-3-486-77585-3 |    1480 | 1702.99 | 1213.99 |
| 978-3-486-76396-6 |    1480 | 1702.99 | 1213.99 |
| 978-3-486-78098-7 |     200 |  280    |  150.99 |
| 978-3-486-71115-8 |     430 |  602    |  322.99 |
| 978-3-11-034025-9 |     450 |  630    |  337.99 |

Candidate column headers:
bl_eur
isbn
bl_usd
bl_gbp



headers: bl_eur
isbn
bl_usd
bl_gbp
label: isbn
bl_eur
bl_usd
bl_gbp
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        |   Col_1 | Col_2      |   Col_3 |
|:-------------|--------:|:-----------|--------:|
| CHIC:TEDS    |    2825 | UK         |      12 |
| CHIC:LBC1936 |     947 | UK         |      11 |
| CHIC:QIMR    |    1752 | Australian |      17 |
| CHIC:LBC1921 |     464 | UK         |      11 |
| CHIC:ALSPAC  |    5517 | UK         |       8 |

Candidate column headers:
Cohort
N
Mean age
Population



headers: Cohort
N
Mean age
Population
label: Cohort
N
Population
Mean age
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                          |   Col_1 | Col_2      | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   | Col_13   | Col_14   | Col_15             | Col_16             | Col_17   | Col_18             |
|:-------------------------------|--------:|:-----------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|:---------|:---------|:-------------------|:-------------------|:---------|:-------------------|
| Commercial and Public Services |    2013 | Non-traded | n       | -       | -       | -       | -       | 0.1     | 0.17    | 0.24     | 0.31     | 0.41     | 0.52     | 0.64     | 0.75               | 0.88               | 1.01     | 1.1499999999999999 |
| Commercial and Public Services |    2012 | Non-traded | n       |         | -       | -       | 0       | 0.1     | 0.2     | 0.3      | 0.4      | 0.5      | 0.6      | 0.8      | 0.9                | 1.1000000000000001 | 1.2      | 1.4                |
| Agriculture and Waste          |    2011 | Non-traded | n       |         | -       | -       | -       | -       | -       | -        | -        | -        | 0.6      | 1.5      | 2.2000000000000002 | 2.7                | 3.2      | 3.4                |
| Industry                       |    2012 | Traded     | n       |         | -       | -       | -       | -       | -       | -        | -        | -        | -        | -        | -                  | -                  | -        | -                  |
| Agriculture and Waste          |    2012 | Non-traded | n       |         | -       | -       | -       | -       | -       | -        | -        | -        | 0.6      | 1.5      | 2.2000000000000002 | 2.7                | 3.2      | 3.4                |

Candidate column headers:
2016
2020
2019
Year of UEP
2009
Traded/non-traded
2015
2011
2007
2017
2021
2018
Sector
2013
2012
2014
2010
2008
Baseline



headers: 2016
2020
2019
Year of UEP
2009
Traded/non-traded
2015
2011
2007
2017
2021
2018
Sector
2013
2012
2014
2010
2008
Baseline
label: Sector
Year of UEP
Traded/non-traded
Baseline
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|   41646 |      70 |      48 |
|   41648 |      62 |      30 |
|   41641 |      80 |      43 |
|   41656 |      44 |       7 |
|   41649 |       5 |      24 |

Candidate column headers:
Date
ValeurA
ValeurB



headers: Date
ValeurA
ValeurB
label: Date
ValeurA
ValeurB
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |          Col_7 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------------:|
|    0.93 |    0.79 |    0.94 |    0.71 |    3.28 |     791 |  0.335  | 440700         |
|    0.77 |    0.76 |    0.73 |    0.52 |    2.82 |    3231 |  0.5091 |      7.302e+06 |
|    0.79 |    0.83 |    0.77 |    0.51 |    2.92 |    3015 |  0.5244 |      3.201e+06 |
|    0.7  |    0.71 |    0.7  |    0.31 |    2.71 |    1247 |  0.672  |      7.301e+06 |
|    0.74 |    0.54 |    0.53 |    0.21 |    2.47 |     503 |  0.827  |      2.901e+06 |

Candidate column headers:
District LEA
District Enrollment
Total % Proficient or Advanced  Literacy
Total % Proficient or Advanced Algebra
Total % Proficient or Advanced  Geometry
District Overall GPA
District % FRL
Total % Proficient or Advanced  Biology



headers: District LEA
District Enrollment
Total % Proficient or Advanced  Literacy
Total % Proficient or Advanced Algebra
Total % Proficient or Advanced  Geometry
District Overall GPA
District % FRL
Total % Proficient or Advanced  Biology
label: Total % Proficient or Advanced Algebra
Total % Proficient or Advanced  Geometry
Total % Proficient or Advanced  Literacy
Total % Proficient or Advanced  Biology
District Overall GPA
District Enrollment
District % FRL
District LEA
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        |   Col_1 | Col_2   |   Col_3 | Col_4   |   Col_5 |   Col_6 | Col_7   | Col_8   | Col_9   |   Col_10 |   Col_11 | Col_12   |   Col_13 | Col_14            | Col_15       |
|:-------------|--------:|:--------|--------:|:--------|--------:|--------:|:--------|:--------|:--------|---------:|---------:|:---------|---------:|:------------------|:-------------|
| C50A (2q779) |       4 | 0-1     |       0 | 6w      |      30 |      31 | 10      | 10      | b/d     |        5 |       86 | 4L       |     0.2  | Wet Meadow        | Wet          |
| C2A (2q0rr)  |       4 | 0-1     |       0 | 4w      |      35 |      83 | 9w      | 10      | b/d     |        5 |       86 | 4L       |     0.2  | Saline Lowland    | Saline       |
| C54A (2qyg5) |      10 | 0-2     |       1 | 3s      |      20 |      63 | 10      | 10      | b/d     |        5 |       86 | 4L       |     0.2  | Saline Lowland    | Saline       |
| C50A (2q779) |       3 | 0-2     |       1 | 2s      |      65 |      31 | 1k      | 2kk     | c       |        3 |       86 | 4L       |     0.24 | Limy Subirrigated | Subirrigated |
| C2A (2q0rr)  |       5 | 0-3     |       1 | 2c      |     100 |      83 | 1       | 1       | c       |        5 |       48 | 6        |     0.24 | Loamy             | Loam         |

Candidate column headers:
Slope (%)
WEG
Map Unit Symbol (National Symbol)
CTSG
FSG
T
Kw
LCC
Component PI
Composition (%)
Map Unit PI
Ecological Site
WEI
Slope RV (%)
HSG
WSG



headers: Slope (%)
WEG
Map Unit Symbol (National Symbol)
CTSG
FSG
T
Kw
LCC
Component PI
Composition (%)
Map Unit PI
Ecological Site
WEI
Slope RV (%)
HSG
WSG
label: Map Unit Symbol (National Symbol)
Composition (%)
Slope (%)
Slope RV (%)
LCC
Component PI
Map Unit PI
WSG
CTSG
HSG
T
WEI
WEG
Kw
Ecological Site
FSG
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0         | Col_1                    |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 | Col_7   |   Col_8 |   Col_9 |
|:--------------|:-------------------------|--------:|--------:|--------:|--------:|--------:|:--------|--------:|--------:|
| Akuila Uate   | Redlens NSW              |   32056 |      25 |     187 |      96 |    2008 | WI      |       2 |  350000 |
| Tim Grant     | St Marys NSW             |   32190 |      24 |     194 |     109 |    2007 | PR      |       1 |  150000 |
| Steve Turner  | Bridgetown NSW           |   30965 |      28 |     177 |      84 |    2002 | WI      |       1 |  150000 |
| Josh McCrone  | Temora NSW               |   31879 |      25 |     180 |      88 |    2009 | FE_ HB  |       2 |  175000 |
| Isaac De Gois | All Saints Liverpool NSW |   31040 |      28 |     180 |      87 |    2006 | HK      |       2 |  100000 |

Candidate column headers:
D.O.B
Years  
 Age    
Contract Value
Position[s]
 W [kg]    
Junior Club  
Debut
 H [cm]    
Name



headers: D.O.B
Years  
 Age    
Contract Value
Position[s]
 W [kg]    
Junior Club  
Debut
 H [cm]    
Name
label: Name
Junior Club  
D.O.B
 Age    
 H [cm]    
 W [kg]    
Debut
Position[s]
Years  
Contract Value
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1   | Col_2   | Col_3   | Col_4   | Col_5             | Col_6   | Col_7              | Col_8   |
|:--------|:--------|:--------|:--------|:--------|:------------------|:--------|:-------------------|:--------|
| DtC2    |         |         |         |         |                   | HEL     |                    | Dp_Th   |
| Ak      |         |         |         |         |                   | NHEL    |                    | Bh      |
| AuE2    |         |         |         |         |                   | HEL     |                    |         |
| Ea      |         |         |         |         |                   | NHEL    |                    | Hr      |
|         | 3.5     | 0.49    | 4.0     | 125.0   | 13.17671183849796 | HEL     | 0.4377645129069089 |         |

Candidate column headers:
K
Category
Old HEL Sym
Map Symbol
EI
% Slope Used
Slope Length Used
T
HEL Minimum LS



headers: K
Category
Old HEL Sym
Map Symbol
EI
% Slope Used
Slope Length Used
T
HEL Minimum LS
label: Map Symbol
T
K
% Slope Used
Slope Length Used
EI
Category
HEL Minimum LS
Old HEL Sym
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 | Col_2                        | Col_3   |
|:--------|--------:|:-----------------------------|:--------|
| 4911603 |   42621 | Mueller Road                 | STP     |
| 4950603 |   42621 | Resurfacing_ Ped Bridge_ ADA | STP     |
| FLAP026 |   42578 | BR Rplmt-Brazil Rd-Hazel Cr  | FLAP    |
| 9901640 |   42621 | Dougherty Ferry Road         | STP     |
| 5555612 |   42621 | Bopp Road                    | STP     |

Candidate column headers:
Prefix
Project Description
Program Award Date
Federal Project Number



headers: Prefix
Project Description
Program Award Date
Federal Project Number
label: Federal Project Number
Program Award Date
Project Description
Prefix
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |
|--------:|--------:|--------:|--------:|--------:|--------:|
|       2 | 10043.8 | 43.9414 | 10087.7 |       0 | 10087.7 |
|      27 | 11201.9 | 49.0085 | 11250.9 |       0 | 11250.9 |
|      40 | 11856   | 51.8702 | 11907.9 |       0 | 11907.9 |
|      19 | 10817.5 | 47.3265 | 10864.8 |       0 | 10864.8 |
|      46 | 12170.7 | 53.2468 | 12223.9 |       0 | 12223.9 |

Candidate column headers:
INTEREST + BALANCE
MONTH
BALANCE
INTEREST EARNED
AMOUNT PAID TO INVESTOR
NEW BALANCE



headers: INTEREST + BALANCE
MONTH
BALANCE
INTEREST EARNED
AMOUNT PAID TO INVESTOR
NEW BALANCE
label: MONTH
BALANCE
INTEREST EARNED
INTEREST + BALANCE
AMOUNT PAID TO INVESTOR
NEW BALANCE
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1     | Col_2     | Col_3   | Col_4       |           Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |
|--------:|:----------|:----------|:--------|:------------|----------------:|--------:|--------:|--------:|--------:|
|    2013 | China     | Insurance | ESS     | Short Term  | 540000          |  270000 |  270000 |       0 |       0 |
|    2010 | Panama    | Guarantee |         | Medium Term |      0          |       0 |       0 |       0 |       0 |
|    2013 | Argentina | Insurance | ESS     | Short Term  |      0          |       0 |       0 |       0 |       0 |
|    2011 | Mexico    | Insurance | FB      | Short Term  |      2.1433e+06 |  540000 |  173880 |       0 |       0 |
|    2008 | Sweden    | Insurance | ESS     | Short Term  | 337500          |  337500 |  337500 |       0 |       0 |

Candidate column headers:
Policy Type
Approved/Declined Amount
Term
Small Business Authorized Amount
Woman Owned Authorized Amount
Fiscal Year
Country
Total Amount
Program
Minority Owned Authorized Amount



headers: Policy Type
Approved/Declined Amount
Term
Small Business Authorized Amount
Woman Owned Authorized Amount
Fiscal Year
Country
Total Amount
Program
Minority Owned Authorized Amount
label: Fiscal Year
Country
Program
Policy Type
Term
Total Amount
Approved/Declined Amount
Small Business Authorized Amount
Woman Owned Authorized Amount
Minority Owned Authorized Amount
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1       |   Col_2 |   Col_3 |   Col_4 | Col_5   |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 | Col_11   | Col_12      |   Col_13 |
|--------:|:------------|--------:|--------:|--------:|:--------|--------:|--------:|--------:|--------:|---------:|:---------|:------------|---------:|
|       7 | Legislative |       1 |     413 |    3000 | 1-3     |       3 |       3 |     605 |   60503 |        2 | Special  | 02: Special |     2413 |
|       7 | Legislative |       1 |     101 |    1000 | 1-1     |       1 |       1 |     782 |   78204 |        1 | General  | 01: General |     1000 |
|       7 | Legislative |       1 |     101 |    1000 | 1-1     |       1 |       1 |     782 |   78204 |        1 | General  | 01: General |     1000 |
|       7 | Legislative |       1 |     133 |    2000 | 1-2     |       2 |       2 |     783 |   78301 |        1 | General  | 01: General |     1000 |
|       7 | Legislative |       1 |     961 |    4000 | 1-4     |       4 |       4 |     399 |   39923 |        1 | General  | 01: General |     1000 |

Candidate column headers:
Fund Group Name
Agency Code
Program Code
Item
Item Value
Fund Group
Secretarial Area Code
Secretarial Area
Item Number
SecArea Sort
AgySort
Fund Detail Code
Service Area Code
Fund Group Code



headers: Fund Group Name
Agency Code
Program Code
Item
Item Value
Fund Group
Secretarial Area Code
Secretarial Area
Item Number
SecArea Sort
AgySort
Fund Detail Code
Service Area Code
Fund Group Code
label: Secretarial Area Code
Secretarial Area
SecArea Sort
Agency Code
AgySort
Item Number
Item
Item Value
Program Code
Service Area Code
Fund Group Code
Fund Group Name
Fund Group
Fund Detail Code
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        | Col_1                       | Col_2                | Col_3          | Col_4              |   Col_5 |
|:-------------|:----------------------------|:---------------------|:---------------|:-------------------|--------:|
| toggleButton | None (Core Tab)             | TabHome              | GroupFont      |                    |     114 |
| button       | None (Core Tab)             | TabHome              | GroupClipboard | PasteMenu          |     755 |
| toggleButton | None (Quick Access Toolbar) | Quick Access Toolbar |                |                    |    2059 |
| button       | None (Core Tab)             | TabHome              | GroupClipboard |                    |      21 |
| button       | None (Core Tab)             | TabHome              | GroupFont      | TextEffectsGallery |   14837 |

Candidate column headers:
Tab
Parent Control
Control Type
Tab Set
Group/Context Menu Name
Policy ID



headers: Tab
Parent Control
Control Type
Tab Set
Group/Context Menu Name
Policy ID
label: Control Type
Tab Set
Tab
Group/Context Menu Name
Parent Control
Policy ID
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        |   Col_1 |   Col_2 |   Col_3 |         Col_4 |
|:-------------|--------:|--------:|--------:|--------------:|
| Business     |   14.47 |  9.4055 | 10.8525 | 9781292196930 |
| Childcare    |   15.87 | 10.3155 | 11.9025 | 9781292197081 |
| Business     |   26.5  | 17.225  | 19.875  | 9781446901366 |
| Construction |   32.66 | 21.229  | 24.495  | 9781292184043 |
| Childcare    |   26.78 | 17.407  | 20.085  | 9781447970965 |

Candidate column headers:
Current price
26 Nov -25%
23 Nov -35%
ISBN
Subject



headers: Current price
26 Nov -25%
23 Nov -35%
ISBN
Subject
label: Subject
Current price
23 Nov -35%
26 Nov -25%
ISBN
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                        |   Col_1 |   Col_2 |
|:-----------------------------|--------:|--------:|
| Paediatrics and Child Health |       2 |       1 |
| Paediatrics and Child Health |       1 |       1 |
| Paediatrics and Child Health |       3 |       1 |
| Medicine                     |       2 |       1 |
| Paediatrics and Child Health |       5 |       1 |

Candidate column headers:
Placements available
Block
Discipline



headers: Placements available
Block
Discipline
label: Discipline
Block
Placements available
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0      | Col_1      | Col_2   |   Col_3 | Col_4    | Col_5          | Col_6   | Col_7      | Col_8   | Col_9      | Col_10   | Col_11     | Col_12     | Col_13     |
|:-----------|:-----------|:--------|--------:|:---------|:---------------|:--------|:-----------|:--------|:-----------|:---------|:-----------|:-----------|:-----------|
| No Comment | No Comment |         |       2 | Leonardo | Access Florida | Pass    | No Comment | Pass    | No Comment | Pass     | No Comment | No Comment | No Comment |
| No Comment | No Comment | Pass    |      18 | Leonardo | Access Florida | Pass    | No Comment | Pass    | No Comment | Pass     | No Comment | No Comment | No Comment |
| No Comment | No Comment | Pass    |      23 | Leonardo | Access Florida | Pass    | No Comment | Pass    | No Comment | Pass     | No Comment | No Comment | No Comment |
| No Comment | No Comment | Pass    |      28 | Leonardo | Access Florida | Pass    | No Comment | Pass    | No Comment | Pass     | No Comment | No Comment | No Comment |
| No Comment | No Comment | Pass    |      38 | Leonardo | Access Florida | Pass    | No Comment | Pass    | No Comment | Pass     | No Comment | No Comment | No Comment |

Candidate column headers:
TextEquivalent
ColorTest
TimedResponses
ServerSideImageMapsComments
SynchronizedMultimediaComments
SkipNavigationComments
StyleSheetsComments
StyleSheets
ColorTestComments
ClientSideImageMapsComments
Tester
Website
TimedResponsesComments
TestingResultsID



headers: TextEquivalent
ColorTest
TimedResponses
ServerSideImageMapsComments
SynchronizedMultimediaComments
SkipNavigationComments
StyleSheetsComments
StyleSheets
ColorTestComments
ClientSideImageMapsComments
Tester
Website
TimedResponsesComments
TestingResultsID
label: SkipNavigationComments
TimedResponsesComments
TimedResponses
TestingResultsID
Tester
Website
TextEquivalent
SynchronizedMultimediaComments
ColorTest
ColorTestComments
StyleSheets
StyleSheetsComments
ServerSideImageMapsComments
ClientSideImageMapsComments
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        | Col_1   |   Col_2 | Col_3     |   Col_4 | Col_5             |   Col_6 |   Col_7 |   Col_8 |   Col_9 | Col_10           |
|:-------------|:--------|--------:|:----------|--------:|:------------------|--------:|--------:|--------:|--------:|:-----------------|
| 04-41-B10521 | GMIS    |    2011 | Submitted | 40926.6 |                   | 40968.6 | 40938.7 | 40938.7 |       1 | 04-41-B105212011 |
| 01-43-B10577 | GMIS    |    2012 | Submitted | 40947.5 | 41318.6590212963  | 41326.5 | 41324.7 | 40949.6 |       1 | 01-43-B105772012 |
| 06-42-B10579 | GMIS    |    2013 | Submitted | 41541.6 |                   | 41618.5 | 41572.8 | 41572.8 |       1 | 06-42-B105792013 |
| 06-42-B10009 | GMIS    |    2010 | Submitted | 40563.5 | 40582.71748684028 | 40600.2 | 40592.5 | 40570.7 |       1 | 06-42-B100092010 |
| 06-42-B10579 | GMIS    |    2012 | Submitted | 41260.8 | 41375.72787947917 | 41382.6 | 41380.8 | 41304.1 |       1 | 06-42-B105792012 |

Candidate column headers:
Original Submission Date
Originally Submitted After April 11_ 2014
Submit_Date
Column1
Date Created
System_Name
Return_Date
Year_Desc
Award_Num
Accepted_Date
Report Status



headers: Original Submission Date
Originally Submitted After April 11_ 2014
Submit_Date
Column1
Date Created
System_Name
Return_Date
Year_Desc
Award_Num
Accepted_Date
Report Status
label: Award_Num
System_Name
Year_Desc
Report Status
Date Created
Return_Date
Accepted_Date
Submit_Date
Original Submission Date
Originally Submitted After April 11_ 2014
Column1
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0               | Col_1   |   Col_2 |   Col_3 |   Col_4 | Col_5   |   Col_6 |   Col_7 |
|:--------------------|:--------|--------:|--------:|--------:|:--------|--------:|--------:|
| Cognizant           | NYC     |   43136 |   43171 |      35 | 0.2     |     0   |     0   |
| Voya                | NYC     |   43129 |   43164 |      35 | 1.0     |    35   |     0   |
| Principal Financial | LA      |   43213 |   43304 |      91 |         |     0   |    91   |
| Signet              | NYC     |   43252 |   43304 |      52 |         |     0   |    52   |
| Intel (Global)      | Chicago |   43160 |   43251 |      91 | 0.2     |    18.2 |    72.8 |

Candidate column headers:
Complete
Remaining
Total Days
End
Account
% Complete
Start
Region



headers: Complete
Remaining
Total Days
End
Account
% Complete
Start
Region
label: Account
Region
Start
End
Total Days
% Complete
Complete
Remaining
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0             |   Col_1 | Col_2            | Col_3   |
|:------------------|--------:|:-----------------|:--------|
| High Capacity     |    5.45 | 1000012359 (Ln2) | 18x24x1 |
| High Capacity     |    5.78 | 1000012359 (Ln2) | 16x20x2 |
| Standard Capacity |    6.5  | 1000000044 (Ln1) | 25x25x2 |
| High Capacity     |    8.02 | 1000012359 (Ln2) | 24x24x2 |
| High Capacity     |    7.13 | 1000012359 (Ln2) | 18x24x2 |

Candidate column headers:
Filter Type
Size
Price
Item ID



headers: Filter Type
Size
Price
Item ID
label: Filter Type
Price
Item ID
Size
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0       |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   |
|:------------|--------:|:--------|:--------|:--------|:--------|:--------|
| Angola      |    2000 | -       | -       |         | 99      |         |
| Afghanistan |    2007 | 92      | 92      |         | 94      |         |
| Azerbaijan  |    2013 | 87      | 87      | w       | 87      | w       |
| Azerbaijan  |    2012 | 90      | 92      | w       | 90      | w       |
| Afghanistan |    2012 | -       | -       |         | -       |         |

Candidate column headers:
semester 1 coverage
footnoteS2
footnoteS1
two dose coverage
semester 2 coverage
year
Country



headers: semester 1 coverage
footnoteS2
footnoteS1
two dose coverage
semester 2 coverage
year
Country
label: Country
year
two dose coverage
semester 1 coverage
footnoteS1
semester 2 coverage
footnoteS2
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |   Col_18 |
|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|       1 |       3 |       4 |    3.02 |       8 |      27 |      14 |      13 |      27 |      12 |       15 |       56 |       73 |        5 |       67 |       24 |       26 |       17 |       93 |
|       1 |       5 |       4 |    3    |       8 |      32 |      15 |      17 |      25 |       9 |       16 |       60 |       78 |        4 |       52 |       23 |       19 |       10 |      104 |
|       1 |       2 |       2 |    1.58 |       7 |      23 |       9 |      14 |      30 |      15 |       15 |       29 |       51 |        4 |       72 |       26 |       29 |       17 |       95 |
|       1 |       4 |       2 |    3.12 |       7 |      28 |      14 |      14 |      30 |      15 |       15 |       60 |       60 |        4 |       76 |       28 |       28 |       20 |       95 |
|       1 |       2 |       2 |    3.15 |       4 |      19 |       8 |      11 |      21 |       9 |       12 |       47 |       70 |        4 |       74 |       31 |       25 |       18 |      100 |

Candidate column headers:
alienation
a_mot
csoc_com
ssoc_com
p_learning
gpa
s_community
c_community
ethnicity
extr_mot
gender
acad_self_concept
isolation
norml
intr_mot
slrn_com
clrn_com
age
powerl



headers: alienation
a_mot
csoc_com
ssoc_com
p_learning
gpa
s_community
c_community
ethnicity
extr_mot
gender
acad_self_concept
isolation
norml
intr_mot
slrn_com
clrn_com
age
powerl
label: gender
age
ethnicity
gpa
p_learning
c_community
csoc_com
clrn_com
s_community
ssoc_com
slrn_com
intr_mot
extr_mot
a_mot
alienation
isolation
powerl
norml
acad_self_concept
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0    |   Col_1 |   Col_2 |   Col_3 |   Col_4 | Col_5         |
|:---------|--------:|--------:|--------:|--------:|:--------------|
| GUERNSEY |      39 |      28 | 71.7949 |    1.3  | (0.61 - 2.78) |
| N54      |    3023 |    2049 | 67.7803 |    0.9  | (0.83 - 0.98) |
| N61      |     815 |     561 | 68.8344 |    1.08 | (0.91 - 1.29) |
| N58      |    2304 |    1544 | 67.0139 |    0.87 | (0.79 - 0.96) |
| N53      |    4122 |    2949 | 71.5429 |    1.14 | (1.06 - 1.23) |

Candidate column headers:
OR*
HistologicalDiagnosis_percent
95%CI
networkfirstseen
Histologically confirmed Cases
NumberOfCases



headers: OR*
HistologicalDiagnosis_percent
95%CI
networkfirstseen
Histologically confirmed Cases
NumberOfCases
label: networkfirstseen
NumberOfCases
Histologically confirmed Cases
HistologicalDiagnosis_percent
OR*
95%CI