mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
181 lines
9.6 KiB
Python
181 lines
9.6 KiB
Python
|
# flake8: noqa: E501
|
|||
|
|
base_prompt_dict = {
|
|||
|
|
'basic':
|
|||
|
|
"""You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'basic-CN':
|
|||
|
|
"""给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'adversarial-ignore':
|
|||
|
|
"""You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'adversarial-ignore-CN':
|
|||
|
|
"""给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'adversarial-doubt':
|
|||
|
|
"""You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'adversarial-doubt-CN':
|
|||
|
|
"""给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'zero-shot-IcL':
|
|||
|
|
"""Determine whether the causal effect can be identified given two variables on a causal graph.
|
|||
|
|
You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'zero-shot-IcL-CN':
|
|||
|
|
"""确定在因果图中给定两个变量的情况下，因果效应是否可以被识别。
|
|||
|
|
给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'one-shot-IcL':
|
|||
|
|
"""Determine whether the causal effect can be identified given two variables on a causal graph.
|
|||
|
|
You will be presented with a causal graph in the following form: A causes E, A causes C, A causes B, B causes D, B causes E, and D causes E.
|
|||
|
|
There exist unobserved confounders between: B and E.
|
|||
|
|
Question: Whether the causal effect of B on E is identified or not?
|
|||
|
|
Answer (Yes or No ?): No
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'one-shot-IcL-CN':
|
|||
|
|
"""确定在因果图中给定两个变量的情况下，因果效应是否可以被识别。
|
|||
|
|
给定如下因果图：A导致E, A导致C, A导致B, B导致D, B导致E, 以及D导致E。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：B和E。
|
|||
|
|
问题：B对E的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：否
|
|||
|
|
|
|||
|
|
给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'three-shot-IcL':
|
|||
|
|
"""Determine whether the causal effect can be identified given two variables on a causal graph.
|
|||
|
|
You will be presented with a causal graph in the following form: A causes E, A causes C, A causes B, B causes D, B causes E, and D causes E.
|
|||
|
|
There exist unobserved confounders between: B and E.
|
|||
|
|
Question: Whether the causal effect of B on E is identified or not?
|
|||
|
|
Answer (Yes or No ?): No
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: A causes D, A causes E, B causes E, C causes D, and D causes E.
|
|||
|
|
There exist unobserved confounders between: C and D, and A and E.
|
|||
|
|
Question: Whether the causal effect of C on D is identified or not?
|
|||
|
|
Answer (Yes or No ?): No
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: A causes D, A causes C, A causes B, B causes E, B causes D, and C causes D.
|
|||
|
|
There exist unobserved confounders between: B and D, C and D, and A and B.
|
|||
|
|
Question: Whether the causal effect of D on C is identified or not?
|
|||
|
|
Answer (Yes or No ?): Yes
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'three-shot-IcL-CN':
|
|||
|
|
"""确定在因果图中给定两个变量的情况下，因果效应是否可以被识别。
|
|||
|
|
给定如下因果图：A导致E, A导致C, A导致B, B导致D, B导致E, 以及D导致E。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：B和E。
|
|||
|
|
问题：B对E的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：否
|
|||
|
|
|
|||
|
|
给定如下因果图：A导致D, A导致E, B导致E, C导致D, 以及D导致E。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：C和D, 以及A和E。
|
|||
|
|
问题：C对D的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：否
|
|||
|
|
|
|||
|
|
给定如下因果图：A导致D, A导致C, A导致B, B导致E, B导致D, 以及C导致D。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：B和D, C和D, 以及A和B。
|
|||
|
|
问题：D对C的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：是
|
|||
|
|
|
|||
|
|
给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'zero-shot-CoT':
|
|||
|
|
"""You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not? Let's think step by step.
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'zero-shot-CoT-CN':
|
|||
|
|
"""给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？请逐步思考。
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
'manual-CoT':
|
|||
|
|
"""Here are three examples of causal effect identification using chain of thought, and a question to answer.
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: A causes E, A causes D, B causes D, B causes E, C causes E, and D causes E.
|
|||
|
|
There exist unobserved confounders between: B and D.
|
|||
|
|
Question: Whether the causal effect of B on E is identified or not?
|
|||
|
|
Answer (Yes or No ?): The unobserved confounders between B and D suggests there might be a causal path from the confounder to B. Therefore, there may be an unblocked back-door path from B to E, making the causal effect of B on E not identified. Therefore, the answer is No.
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: A causes B, B causes C, B causes D, and D causes E.
|
|||
|
|
There exist unobserved confounders between: .
|
|||
|
|
Question: Whether the causal effect of A on B is identified or not?
|
|||
|
|
Answer (Yes or No ?): There are no unobserved confounders, and there is no unblocked back-door path from A to B, so the causal effect of A on B can be identified. Therefore, the answer is Yes.
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: A causes D, A causes C, B causes D, B causes E, and C causes D.
|
|||
|
|
There exist unobserved confounders between: B and D, and C and D.
|
|||
|
|
Question: Whether the causal effect of A on B is identified or not?
|
|||
|
|
Answer (Yes or No ?): There are no unobserved confounders between A and B, and there is no unblocked back-door path from A to B, so the causal effect of A on B can be identified. Therefore, the answer is Yes.
|
|||
|
|
|
|||
|
|
You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):
|
|||
|
|
""",
|
|||
|
|
'manual-CoT-CN':
|
|||
|
|
"""如下为两个使用思维链进行推理的判断因果效应可否识别的示例，和一个需要回答的问题。
|
|||
|
|
|
|||
|
|
给定如下因果图：A导致E, A导致D, B导致D, B导致E, C导致E, 以及D导致E。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：B和D。
|
|||
|
|
问题：B对E的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：B和D之间存在不可观察的混淆变量说明可能存在从混淆变量指向B的因果路径。因此B到E可能存在无法被阻断的后门路径，导致B对E的因果效应不可被识别。因此答案为“否”。
|
|||
|
|
|
|||
|
|
给定如下因果图：A导致B, B导致C, B导致D, 以及D导致E。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：。
|
|||
|
|
问题：A对B的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：不存在不可观察的混淆变量，A到B不存在无法被阻断的后门路径，所以A对B的因果效应可以被识别。因此答案为“是”。
|
|||
|
|
|
|||
|
|
给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：
|
|||
|
|
""",
|
|||
|
|
'explicit-function':
|
|||
|
|
"""You are a helpful assistant for causality identification.
|
|||
|
|
You will be presented with a causal graph in the following form: %s.
|
|||
|
|
There exist unobserved confounders between: %s.
|
|||
|
|
Question: Whether the causal effect of %s on %s is identified or not?
|
|||
|
|
Answer (Yes or No ?):""",
|
|||
|
|
'explicit-function-CN':
|
|||
|
|
"""你是一个用于因果识别的得力助手。
|
|||
|
|
给定如下因果图：%s。
|
|||
|
|
在这些变量间存在着不可观察的混淆变量：%s。
|
|||
|
|
问题：%s对%s的因果效应是否可以被识别？
|
|||
|
|
答案（是或否？）：""",
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_prompt(task_name, prompt_style, item, prompt_style_str=''):
|
|||
|
|
base = base_prompt_dict[prompt_style]
|
|||
|
|
|
|||
|
|
prompt = prompt_style_str + base % (item['di_edges'], item['bi_edges'],
|
|||
|
|
item['treatment'], item['outcome'])
|
|||
|
|
return prompt
|