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: tweet_id,airline_sentiment,airline_sentiment_confidence,negativereason,negativereason_confidence,airline,airline_sentiment_gold,name,negativereason_gold,retweet_count\n567849102731526144,negative,1.0,Customer Service Iss,1.0,US Airways,,TerriHaisten,,0\n568210087212388353,neutral,1.0,,,Southwest,,livvyports16,,1\n569824906638073856,negative,1.0,Bad Flight,0.3451,United,,bmalones44,,1\n569558589628502016,negative,0.6927,Can't Tell,0.6927,United,,4geiger,,0\n569627744021184513,negative,1.0,Cancelled Flight,0.6673,American,,MatthewJMedlin,,0\n568809369678315521,negative,1.0,Cancelled Flight,1.0,US Airways,,JeffreyWhitmore,,0\n569456828511326208,negative,1.0,Late Flight,0.6478,US Airways,,CJLarcheveque,,0\n569615736387325952,negative,1.0,Bad Flight,0.3487,Southwest,,Ekanewilliams,,0\n568519360953716736,neutral,1.0,,,Southwest,,MikeWJZ,,1\n569638848214507520,positive,1.0,,,Delta,,oggito17,,0\n569275566077165568,neutral,1.0,,,United,,SallyM0nster,,0\n569826992251473921,neutral,0.6471,,0.0,United,,ohlesliebarker,,0\n569598614235942912,negative,1.0,Late Flight,1.0,Southwest,,BattleB_studios,,0\n568460037737324545,neutral,1.0,,,United,,JerseyRic,,0\n568491905903939584,negative,1.0,Customer Service Iss,0.6579,US Airways,,jekyllandheid12,,0\n \n CSV Table B: 3sk7jMfQzck,NYLj0y6YLFA,AG1gKyPX4RQ,QgYMUapyJlU,7dYptJU3eKE,c2A+LJlP174,6lLeTaOQ74g,DAzjs8gwVB0\nUS Airways,0,5.0 out of 5 stars,0,24591000,,Weak,2024-04-23T05:00:01.\nAmerican,0,5.0 out of 5 stars,0,8334800,,Weak,2024-04-23T05:00:01.\nDelta,0,5.0 out of 5 stars,0,9875400,,Weak,2024-04-23T05:00:01.\nAmerican,0,5.0 out of 5 stars,0,8338300,,New,2024-04-23T05:00:01.\nUnited,0,5.0 out of 5 stars,0,8995500,,Weak,2024-04-23T05:00:01.\nAmerican,0,4.0 out of 5 stars,0,8564500,,New,2024-04-23T05:00:01.\nDelta,0,5.0 out of 5 stars,0,8948500,,New,2024-04-23T05:00:01.\nUnited,0,5.0 out of 5 stars,0,11859900,,New,2024-04-23T05:00:01.\nAmerican,0,5.0 out of 5 stars,0,16537400,,Weak,2024-04-23T05:00:01.\nDelta,0,5.0 out of 5 stars,0,11010400,,New,2024-04-23T05:00:01.\nUS Airways,0,5.0 out of 5 stars,0,7534000,,New,2024-04-23T05:00:01.\nSouthwest,0,5.0 out of 5 stars,0,9818100,,Weak,2024-04-23T05:00:01.\nAmerican,0,5.0 out of 5 stars,0,9965000,,Weak,2024-04-23T05:00:01.\nUnited,0,5.0 out of 5 stars,0,20254600,,Good,2024-04-23T05:00:01.\nUnited,0,5.0 out of 5 stars,1,9989300,,New,2024-04-23T05:00:01.\n \n Output: \n

答案

模型 模型答案 正确答案
金声玉亮2.0 ```python
{'airline': '3sk7jMfQzck'}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
deepseek-r1

{'airline': '3sk7jMfQzck'}		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
gpto1 ```python
{'airline': '3sk7jMfQzck', 'retweet_count': 'QgYMUapyJlU'}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
gpt-4o-2024-08-06 ```python
{
"airline": "3sk7jMfQzck"
}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
DeepSeek-Coder-V2-Lite-Instruct ```python
{
"tweet_id": "3sk7jMfQzck",
"airline_sentiment": "NYLj0y6YLFA",
"airline_sentiment_confidence": "AG1gKyPX4RQ",
"negativereason": "QgYMUapyJlU",
"negativereason_confidence": "7dYptJU3eKE",
"airline": "c2A+LJlP174",
"airline_sentiment_gold": "6lLeTaOQ74g",
"name": "DAzjs8gwVB0",
"negativereason_gold": "c2A+LJlP174",
"retweet_count": "6lLeTaOQ74g"
}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
Meta-Llama-3.1-405B-Instruct-Turbo {'airline': '3sk7jMfQzck'} {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
Meta-Llama-3.1-70B-Instruct-Turbo {'airline': '3sk7jMfQzck'} {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
Qwen2-72B-Instruct {'airline': 'US Airways', 'airline': 'American', 'airline': 'Delta', 'airline': 'United', 'airline': 'Southwest'}

However, this is not a one-to-one mapping as requested. A more appropriate mapping would be:

{'airline': 'airline'}

But since each column in A should map to 0 or 1 columns in B, and given that Table B does not have column headers that match those in Table A, an accurate mapping cannot be created. The airline column in Table A could potentially map to the first column in Table B (assuming it represents the airlines), but without further information, this is speculative.

If we were to make a mapping based on the data rather than the headers, it would look something like this, assuming the first column in Table B is the airline:

{'airline': '3sk7jMfQzck'},
{'airline': 'NYLj0y6YLFA'},
{'airline': 'AG1gKyPX4RQ'},
{'airline': 'QgYMUapyJlU'},
{'airline': '7dYptJU3eKE'},
{'airline': 'c2A+LJlP174'},
{'airline': '6lLeTaOQ74g'},
{'airline': 'DAzjs8gwVB0'}

But again, this is purely speculative and not a true column-to-column mapping as requested.
In reality, the correct answer would be that no valid mapping can be created with the provided data and instructions.		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
baichuan4 {
"airline": "US Airways"
}		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
doubao {"airline": "3sk7jMfQzck"} {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
gpto1 ```python
{'airline': '3sk7jMfQzck', 'retweet_count': 'QgYMUapyJlU'}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
kimi {
"airline": "0",
"airline_sentiment": None,
"airline_sentiment_confidence": None,
"negativereason": None,
"negativereason_confidence": None,
"airline_sentiment_gold": None,
"name": None,
"negativereason_gold": None,
"retweet_count": None
}		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
minimax ```python
{
'airline': '3sk7jMfQzck',
'airline_sentiment': 'AG1gKyPX4RQ',
'airline_sentiment_confidence': 'QgYMUapyJlU',
'negativereason': '7dYptJU3eKE',
'negativereason_confidence': 'c2A+LJlP174',
'retweet_count': '6lLeTaOQ74g'
}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
wenxinyiyan ```python
{'airline': '3sk7jMfQzck'}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
zhipu ```python
{
'airline': '3sk7jMfQzck'
}
```		 {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}
deepseek-v3 {'airline': '3sk7jMfQzck'} {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"}