金声玉亮2.0模型评测报告

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: time,power,temp,humidity,light,CO2,dust\n2015-08-06 13:35:30,0.572,34,34,23,1329,6.49\n2015-08-05 08:34:28,0.0,31,40,8,1184,14.42\n2015-08-30 12:00:30,-1.0,34,29,20,2000,9.52\n2015-08-14 05:36:37,0.0,34,33,0,2000,12.63\n2015-08-17 14:26:16,0.0,35,29,11,2000,9.94\n2015-08-11 01:17:52,0.0,33,34,0,2000,25.68\n2015-08-01 01:48:22,0.0,32,41,0,973,25.11\n2015-08-29 18:59:33,-1.0,35,28,23,2000,5.32\n2015-08-09 11:57:26,0.528,32,35,7,1806,10.68\n2015-08-06 06:26:53,0.0,31,38,0,1300,12.87\n2015-08-17 21:01:45,0.0,35,30,26,2000,5.08\n2015-08-06 11:37:33,0.0,34,36,22,1374,14.07\n2015-08-01 23:56:50,0.0,33,40,0,956,20.39\n2015-08-04 10:11:26,0.0,32,39,19,1102,10.26\n2015-08-10 08:12:01,-1.0,33,34,18,2000,15.09\n2015-08-10 12:07:54,0.088,33,33,14,2000,8.53\n \n CSV Table B: +TcFRhetc3o,0bFLf6WxD8A,Y70Tlv14K3Y,5ArEgCtuDyM,9etcI5xa42c\n6040452,15.6466,-1.0,24591000,2024-04-23T05:00:01.\n6038888,15.6466,0.0,8334800,2024-04-23T05:00:01.\n5941356,15.6466,0.0,9875400,2024-04-23T05:00:01.\n6040452,15.6466,-1.0,8338300,2024-04-23T05:00:01.\n5941356,15.6466,-1.0,8995500,2024-04-23T05:00:01.\n5510456,15.6466,-1.0,8564500,2024-04-23T05:00:01.\n6040452,15.6466,0.0,8948500,2024-04-23T05:00:01.\n5510456,15.6466,0.0,11859900,2024-04-23T05:00:01.\n6038888,15.6466,0.11,16537400,2024-04-23T05:00:01.\n5026787,15.6466,0.0,11010400,2024-04-23T05:00:01.\n6040452,15.6466,0.418,7534000,2024-04-23T05:00:01.\n5510456,15.6466,-1.0,9818100,2024-04-23T05:00:01.\n6038888,15.6466,-1.0,9965000,2024-04-23T05:00:01.\n5941356,15.6466,0.0,20254600,2024-04-23T05:00:01.\n5510456,15.6466,0.682,9989300,2024-04-23T05:00:01.\n5026787,15.6466,0.0,12805200,2024-04-23T05:00:01.\n5510456,15.6466,0.0,12652800,2024-04-23T05:00:01.\n \n Output: \n

答案

模型	模型答案	正确答案
金声玉亮2.0	```python {'power': 'Y70Tlv14K3Y'} ```	{"power": "Y70Tlv14K3Y"}
deepseek-r1	{"power": "Y70Tlv14K3Y", "time": "9etcI5xa42c"}	{"power": "Y70Tlv14K3Y"}
gpto1	{'power': 'Y70Tlv14K3Y'}	{"power": "Y70Tlv14K3Y"}
gpt-4o-2024-08-06	```python { "power": "Y70Tlv14K3Y" } ```	{"power": "Y70Tlv14K3Y"}
DeepSeek-Coder-V2-Lite-Instruct	```python { "time": "+TcFRhetc3o", "power": "0bFLf6WxD8A", "temp": "Y70Tlv14K3Y", "humidity": "5ArEgCtuDyM", "light": "9etcI5xa42c" } ```	{"power": "Y70Tlv14K3Y"}
Meta-Llama-3.1-405B-Instruct-Turbo	{'power': 'Y70Tlv14K3Y'}	{"power": "Y70Tlv14K3Y"}
Meta-Llama-3.1-70B-Instruct-Turbo	{'power': 'Y70Tlv14K3Y'}	{"power": "Y70Tlv14K3Y"}
Qwen2-72B-Instruct	{'time': '9etcI5xa42c', 'power': '0bFLf6WxD8A', 'temp': None, 'humidity': None, 'light': None, 'CO2': None, 'dust': None} Note: In this case, only 'time' and 'power' columns from table A can be mapped to columns in table B. The 'time' column can be mapped to '9etcI5xa42c' which seems to represent a timestamp, and 'power' can be mapped to '0bFLf6WxD8A'. The other columns do not have clear matches in table B.	{"power": "Y70Tlv14K3Y"}
baichuan4	Based on the provided CSV tables, it seems that there is no direct relationship between the columns of Table A and Table B. The columns in Table A contain environmental sensor data (time, power, temp, humidity, light, CO2, dust), while the columns in Table B appear to be unrelated identifiers or codes without any clear connection to the data in Table A. Since there is no apparent relationship between the columns of the two tables, it is not possible to create a valid join mapping as requested. A join mapping typically requires at least one column in each table that contains matching or related data, which can be used to combine the rows from both tables based on a common key or condition. If there was a specific relationship or pattern intended between the columns of Table A and Table B, please provide additional information or clarification so that an appropriate join mapping can be created.	{"power": "Y70Tlv14K3Y"}
doubao	{ "time": "9etcI5xa42c" }	{"power": "Y70Tlv14K3Y"}
gpto1	{'power': 'Y70Tlv14K3Y'}	{"power": "Y70Tlv14K3Y"}
kimi	{ "time": "+TcFRhetc3o", "power": "0bFLf6WxD8A", "temp": "Y70Tlv14K3Y", "humidity": "5ArEgCtuDyM", "CO2": "9etcI5xa42c", "light": "0bFLf6WxD8A", "dust": "9etcI5xa42c" }	{"power": "Y70Tlv14K3Y"}
minimax	```python { "time": "5ArEgCtuDyM", "power": "Y70Tlv14K3Y", "temp": "0bFLf6WxD8A", "humidity": "9etcI5xa42c", "light": None, "CO2": None, "dust": None } ```	{"power": "Y70Tlv14K3Y"}
wenxinyiyan	```python { "time": "+TcFRhetc3o", "power": "0bFLf6WxD8A", "temp": "Y70Tlv14K3Y", "humidity": None, "light": None, "CO2": "5ArEgCtuDyM", "dust": "9etcI5xa42c" } ```	{"power": "Y70Tlv14K3Y"}
zhipu	```python { "power": "Y70Tlv14K3Y" } ```	{"power": "Y70Tlv14K3Y"}
deepseek-v3	{"power": "0bFLf6WxD8A", "temp": "Y70Tlv14K3Y"}	{"power": "Y70Tlv14K3Y"}

模型

模型答案

正确答案

金声玉亮2.0

```python
{'power': 'Y70Tlv14K3Y'}
```

{"power": "Y70Tlv14K3Y"}

deepseek-r1

{"power": "Y70Tlv14K3Y", "time": "9etcI5xa42c"}

{"power": "Y70Tlv14K3Y"}

gpto1

{'power': 'Y70Tlv14K3Y'}

{"power": "Y70Tlv14K3Y"}

gpt-4o-2024-08-06

```python
{
"power": "Y70Tlv14K3Y"
}
```

{"power": "Y70Tlv14K3Y"}

DeepSeek-Coder-V2-Lite-Instruct

```python
{
"time": "+TcFRhetc3o",
"power": "0bFLf6WxD8A",
"temp": "Y70Tlv14K3Y",
"humidity": "5ArEgCtuDyM",
"light": "9etcI5xa42c"
}
```

{"power": "Y70Tlv14K3Y"}

Meta-Llama-3.1-405B-Instruct-Turbo

{'power': 'Y70Tlv14K3Y'}

{"power": "Y70Tlv14K3Y"}

Meta-Llama-3.1-70B-Instruct-Turbo

{'power': 'Y70Tlv14K3Y'}

{"power": "Y70Tlv14K3Y"}

Qwen2-72B-Instruct

{'time': '9etcI5xa42c', 'power': '0bFLf6WxD8A', 'temp': None, 'humidity': None, 'light': None, 'CO2': None, 'dust': None}

Note: In this case, only 'time' and 'power' columns from table A can be mapped to columns in table B. The 'time' column can be mapped to '9etcI5xa42c' which seems to represent a timestamp, and 'power' can be mapped to '0bFLf6WxD8A'. The other columns do not have clear matches in table B.

{"power": "Y70Tlv14K3Y"}

baichuan4

Based on the provided CSV tables, it seems that there is no direct relationship between the columns of Table A and Table B. The columns in Table A contain environmental sensor data (time, power, temp, humidity, light, CO2, dust), while the columns in Table B appear to be unrelated identifiers or codes without any clear connection to the data in Table A.

Since there is no apparent relationship between the columns of the two tables, it is not possible to create a valid join mapping as requested. A join mapping typically requires at least one column in each table that contains matching or related data, which can be used to combine the rows from both tables based on a common key or condition.

If there was a specific relationship or pattern intended between the columns of Table A and Table B, please provide additional information or clarification so that an appropriate join mapping can be created.

{"power": "Y70Tlv14K3Y"}

doubao

{
"time": "9etcI5xa42c"
}

{"power": "Y70Tlv14K3Y"}

gpto1

{'power': 'Y70Tlv14K3Y'}

{"power": "Y70Tlv14K3Y"}

kimi

{
"time": "+TcFRhetc3o",
"power": "0bFLf6WxD8A",
"temp": "Y70Tlv14K3Y",
"humidity": "5ArEgCtuDyM",
"CO2": "9etcI5xa42c",
"light": "0bFLf6WxD8A",
"dust": "9etcI5xa42c"
}

{"power": "Y70Tlv14K3Y"}

minimax

```python
{
"time": "5ArEgCtuDyM",
"power": "Y70Tlv14K3Y",
"temp": "0bFLf6WxD8A",
"humidity": "9etcI5xa42c",
"light": None,
"CO2": None,
"dust": None
}
```

{"power": "Y70Tlv14K3Y"}

wenxinyiyan

```python
{
"time": "+TcFRhetc3o",
"power": "0bFLf6WxD8A",
"temp": "Y70Tlv14K3Y",
"humidity": None,
"light": None,
"CO2": "5ArEgCtuDyM",
"dust": "9etcI5xa42c"
}
```

{"power": "Y70Tlv14K3Y"}

zhipu

```python
{
"power": "Y70Tlv14K3Y"
}
```

{"power": "Y70Tlv14K3Y"}

deepseek-v3

{"power": "0bFLf6WxD8A", "temp": "Y70Tlv14K3Y"}

{"power": "Y70Tlv14K3Y"}