问题

Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \n CSV Table A: gender,age,profession,occupation,country_of_residence,urban_rural,owns_car,salary,cost_of_living,marital_status\nFemale,29,Musician,Full-Time,United States,Rural,No,71672,Medium,Single\nFemale,29,Chef,Full-Time,United States,Rural,No,52829,Medium,Married\nFemale,40,Architect,Full-Time,United States,Urban,Yes (Loan),62303,High,Single\nMale,28,Pilot,Full-Time,United States,Urban,Yes (Owned),73258,High,Married\nFemale,40,Doctor,Full-Time,United States,Rural,No,59573,Medium,Single\nMale,26,Musician,Full-Time,United States,Urban,No,88218,High,Single\nMale,29,Marketing Specialist,Full-Time,United States,Urban,Yes (Loan),78838,Medium,Married\nMale,39,Pilot,Full-Time,United States,Urban,Yes (Loan),74197,High,Single\nMale,29,Writer,Full-Time,United States,Rural,Yes (Owned),88437,High,Married\nFemale,38,Pilot,Full-Time,United States,Urban,No,115931,High,Married\nMale,31,Doctor,Full-Time,United States,Rural,No,111470,High,Single\nFemale,40,Doctor,Full-Time,United States,Rural,Yes (Loan),103918,High,Single\nFemale,23,Firefighter,Full-Time,United States,Urban,No,67955,High,Married\nMale,38,Teacher,Full-Time,United States,Urban,No,84761,Medium,Married\nFemale,36,Doctor,Full-Time,United States,Rural,No,89057,High,Single\nFemale,27,Pilot,Full-Time,United States,Rural,Yes (Owned),119808,Medium,Single\nMale,22,Pilot,Full-Time,United States,Urban,No,112298,Medium,Single\nMale,23,Marketing Specialist,Full-Time,United States,Urban,Yes (Loan),71946,Medium,Single\n \n CSV Table B: 8UKIX1iMOZg,lsTuaMKy100,q9mixw71rsY,NWoi+UEeAUY,Krl1e9fqzyc,LB1c5bVtloU,+3hdejHnpQE,x+dSLMV/+GA\n2024-04-23T05:00:01.,76515,32,0,Male,6040452,5.0 out of 5 stars,Architect\n2024-04-23T05:00:01.,99155,28,1,Female,6038888,5.0 out of 5 stars,Architect\n2024-04-23T05:00:01.,49782,32,2,Male,5941356,5.0 out of 5 stars,Pilot\n2024-04-23T05:00:01.,116517,33,3,Female,6040452,5.0 out of 5 stars,Pilot\n2024-04-23T05:00:01.,82120,25,4,Male,5941356,5.0 out of 5 stars,Chef\n2024-04-23T05:00:01.,89186,32,5,Female,5510456,4.0 out of 5 stars,Pilot\n2024-04-23T05:00:01.,61713,38,6,Female,6040452,5.0 out of 5 stars,Firefighter\n2024-04-23T05:00:01.,109924,35,7,Female,5510456,5.0 out of 5 stars,Teacher\n2024-04-23T05:00:01.,70534,25,8,Male,6038888,5.0 out of 5 stars,Doctor\n2024-04-23T05:00:01.,71039,28,9,Male,5026787,5.0 out of 5 stars,Firefighter\n2024-04-23T05:00:01.,103669,39,10,Male,6040452,5.0 out of 5 stars,Writer\n2024-04-23T05:00:01.,107400,40,11,Female,5510456,5.0 out of 5 stars,Doctor\n2024-04-23T05:00:01.,42569,33,12,Male,6038888,5.0 out of 5 stars,Marketing Specialist\n2024-04-23T05:00:01.,57466,27,13,Female,5941356,5.0 out of 5 stars,Teacher\n2024-04-23T05:00:01.,49245,37,14,Female,5510456,5.0 out of 5 stars,Writer\n2024-04-23T05:00:01.,111461,34,15,Male,5026787,5.0 out of 5 stars,Chef\n2024-04-23T05:00:01.,100164,34,16,Female,5510456,5.0 out of 5 stars,Marketing Specialist\n2024-04-23T05:00:01.,106415,26,17,Female,5026787,5.0 out of 5 stars,Writer\n2024-04-23T05:00:01.,102207,36,18,Female,5510456,5.0 out of 5 stars,Doctor\n \n Output: \n

答案

模型 模型答案 正确答案
金声玉亮2.0 ```python
{
"gender": "Krl1e9fqzyc",
"age": "lsTuaMKy100",
"profession": "x+dSLMV/+GA"
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
deepseek-r1

{"gender": "Krl1e9fqzyc", "age": "q9mixw71rsY", "profession": "x+dSLMV/+GA", "owns_car": "NWoi+UEeAUY", "salary": "lsTuaMKy100"}		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
gpto1 {
"gender": "Krl1e9fqzyc",
"age": "q9mixw71rsY",
"profession": "x+dSLMV/+GA",
"salary": "lsTuaMKy100"
}		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
gpt-4o-2024-08-06 ```python
{
"gender": "Krl1e9fqzyc",
"age": "q9mixw71rsY",
"profession": "x+dSLMV/+GA",
"occupation": None,
"country_of_residence": None,
"urban_rural": None,
"owns_car": None,
"salary": "lsTuaMKy100",
"cost_of_living": None,
"marital_status": None
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
DeepSeek-Coder-V2-Lite-Instruct ```python
{
"gender": "8UKIX1iMOZg",
"age": "lsTuaMKy100",
"profession": "q9mixw71rsY",
"occupation": "NWoi+UEeAUY",
"country_of_residence": "Krl1e9fqzyc",
"urban_rural": "LB1c5bVtloU",
"owns_car": "+3hdejHnpQE",
"salary": "x+dSLMV/+GA"
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
Meta-Llama-3.1-405B-Instruct-Turbo {'profession': 'x+dSLMV/+GA'} {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
Meta-Llama-3.1-70B-Instruct-Turbo {'gender': 'gender', 'age': 'age', 'profession': 'profession', 'occupation': None, 'country_of_residence': None, 'urban_rural': None, 'owns_car': None, 'salary': None, 'cost_of_living': None, 'marital_status': None} {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
Qwen2-72B-Instruct {'gender': 'gender', 'profession': 'profession'}
Note: The provided tables do not have common columns that could be used for a one-to-one mapping except for 'gender' and 'profession'. Therefore, only these two columns are included in the output.		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
baichuan4 {
'gender': 'gender',
'age': 'age',
'profession': 'profession',
'occupation': 'occupation',
'country_of_residence': 'country_of_residence',
'urban_rural': 'urban_rural',
'owns_car': 'owns_car',
'salary': 'salary',
'cost_of_living': 'cost_of_living',
'marital_status': 'marital_status'
}		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
doubao {"gender": "Male", "age": "", "profession": "", "occupation": "", "country_of_residence": "", "urban_rural": "", "owns_car": "", "salary": "", "cost_of_living": "", "marital_status": ""} {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
gpto1 {
"gender": "Krl1e9fqzyc",
"age": "q9mixw71rsY",
"profession": "x+dSLMV/+GA",
"salary": "lsTuaMKy100"
}		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
kimi {'gender': '4', 'age': '2', 'profession': '7', 'occupation': '6'} {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
minimax ```python
{
"gender": "Male",
"age": "32",
"profession": "Architect",
"occupation": "Full-Time",
"country_of_residence": "United States",
"urban_rural": "Rural",
"owns_car": "No",
"salary": "76515",
"cost_of_living": "0",
"marital_status": "Architect"
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
wenxinyiyan ```python
{
'gender': '8UKIX1iMOZg',
'age': 'lsTuaMKy100',
'profession': 'NWoi+UEeAUY',
'country_of_residence': 'Krl1e9fqzyc',
'owns_car': 'LB1c5bVtloU'
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
zhipu ```python
{
"gender": "Male",
"profession": "Architect"
}
```		 {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}
deepseek-v3 {'gender': 'Krl1e9fqzyc', 'age': 'q9mixw71rsY', 'profession': 'x+dSLMV/+GA', 'salary': 'lsTuaMKy100'} {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"}