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dataset_loader_attribution.py
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dataset_loader_attribution.py
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import random
import datasets
import tqdm
import pandas as pd
import re
# you can add more datasets here and write your own dataset parsing function
def process_spaces(text):
return text.replace(
' ,', ',').replace(
' .', '.').replace(
' ?', '?').replace(
' !', '!').replace(
' ;', ';').replace(
' \'', '\'').replace(
' ’ ', '\'').replace(
' :', ':').replace(
'<newline>', '\n').replace(
'`` ', '"').replace(
' \'\'', '"').replace(
'\'\'', '"').replace(
'.. ', '... ').replace(
' )', ')').replace(
'( ', '(').replace(
' n\'t', 'n\'t').replace(
' i ', ' I ').replace(
' i\'', ' I\'').replace(
'\\\'', '\'').replace(
'\n ', '\n').strip()
def process_text_truthfulqa_adv(text):
if "I am sorry" in text:
first_period = text.index('.')
start_idx = first_period + 2
text = text[start_idx:]
if "as an AI language model" in text or "As an AI language model" in text:
first_period = text.index('.')
start_idx = first_period + 2
text = text[start_idx:]
return text
def load_TruthfulQA(cache_dir):
f = pd.read_csv("datasets/TruthfulQA_LLMs.csv")
q = f['Question'].tolist()
a_human = f['Best Answer'].tolist()
mgt_text_list = []
for detectLLM in [
"ChatGPT",
"ChatGLM",
"Dolly",
"ChatGPT-turbo",
"GPT4",
"StableLM"]:
mgt_text_list.append(f[f'{detectLLM}_answer'].fillna("").tolist())
c = f['Category'].tolist()
res = []
for i in range(len(q)):
if len(a_human[i].split()) <= 1:
continue
flag = 1
for mgt_text in mgt_text_list:
if len(mgt_text[i].split()) <= 1 or len(mgt_text[i]) >= 2000:
flag = 0
break
if flag:
res.append([q[i],
a_human[i],
mgt_text_list[0][i],
mgt_text_list[1][i],
mgt_text_list[2][i],
mgt_text_list[3][i],
mgt_text_list[4][i],
mgt_text_list[5][i],
c[i]])
data_new = {
'train': {
'text': [],
'label': [],
'category': [],
},
'test': {
'text': [],
'label': [],
'category': [],
}
}
index_list = list(range(len(res)))
random.seed(0)
random.shuffle(index_list)
total_num = len(res)
for i in tqdm.tqdm(range(total_num), desc="parsing data"):
if i < total_num * 0.8:
data_partition = 'train'
else:
data_partition = 'test'
for j in range(1, 8):
data_new[data_partition]['text'].append(
process_spaces(res[index_list[i]][j]))
data_new[data_partition]['label'].append(j - 1)
return data_new
def load_SQuAD1(cache_dir):
f = pd.read_csv("datasets/SQuAD1_LLMs.csv")
q = f['Question'].tolist()
a_human = [eval(_)['text'][0] for _ in f['answers'].tolist()]
mgt_text_list = []
for detectLLM in [
"ChatGPT",
"ChatGLM",
"Dolly",
"ChatGPT-turbo",
"GPT4",
"StableLM"]:
mgt_text_list.append(f[f'{detectLLM}_answer'].fillna("").tolist())
res = []
for i in range(len(q)):
if len(a_human[i].split()) <= 1:
continue
flag = 1
for mgt_text in mgt_text_list:
if len(mgt_text[i].split()) <= 1:
flag = 0
break
if flag:
res.append([q[i],
a_human[i],
mgt_text_list[0][i],
mgt_text_list[1][i],
mgt_text_list[2][i],
mgt_text_list[3][i],
mgt_text_list[4][i],
mgt_text_list[5][i]])
data_new = {
'train': {
'text': [],
'label': [],
},
'test': {
'text': [],
'label': [],
}
}
index_list = list(range(len(res)))
random.seed(0)
random.shuffle(index_list)
total_num = len(res)
for i in tqdm.tqdm(range(total_num), desc="parsing data"):
if i < total_num * 0.8:
data_partition = 'train'
else:
data_partition = 'test'
for j in range(1, 8):
data_new[data_partition]['text'].append(
process_spaces(res[index_list[i]][j]))
data_new[data_partition]['label'].append(j - 1)
return data_new
def load_NarrativeQA(cache_dir):
f = pd.read_csv("datasets/NarrativeQA_LLMs.csv")
q = f['Question'].tolist()
a_human = f['answers'].tolist()
a_human = [_.split(";")[0] for _ in a_human]
mgt_text_list = []
for detectLLM in [
"ChatGPT",
"ChatGLM",
"Dolly",
"ChatGPT-turbo",
"GPT4",
"StableLM"]:
mgt_text_list.append(f[f'{detectLLM}_answer'].fillna("").tolist())
res = []
for i in range(len(q)):
if len(a_human[i].split()) <= 1 or len(a_human[i].split()) >= 150:
continue
flag = 1
for mgt_text in mgt_text_list:
if len(
mgt_text[i].split()) <= 1 or len(
mgt_text[i].split()) >= 150:
flag = 0
break
if flag:
res.append([q[i],
a_human[i],
mgt_text_list[0][i],
mgt_text_list[1][i],
mgt_text_list[2][i],
mgt_text_list[3][i],
mgt_text_list[4][i],
mgt_text_list[5][i]])
data_new = {
'train': {
'text': [],
'label': [],
},
'test': {
'text': [],
'label': [],
}
}
index_list = list(range(len(res)))
random.seed(0)
random.shuffle(index_list)
total_num = len(res)
for i in tqdm.tqdm(range(total_num), desc="parsing data"):
if i < total_num * 0.8:
data_partition = 'train'
else:
data_partition = 'test'
for j in range(1, 8):
data_new[data_partition]['text'].append(
process_spaces(res[index_list[i]][j]))
data_new[data_partition]['label'].append(j - 1)
return data_new
def load(name):
f = pd.read_csv(f"datasets/{name}_LLMs.csv")
a_human = f["human"].tolist()
mgt_text_list = []
# we do not consider chatgpt in this case
for detectLLM in [
"ChatGLM",
"Dolly",
"ChatGPT-turbo",
"GPT4All",
"StableLM",
"Claude"]:
mgt_text_list.append(f[f'{detectLLM}'].fillna("").tolist())
res = []
for i in range(len(a_human)):
flag = 1
if len(a_human[i].split()) <= 1:
flag = 0
for mgt_text in mgt_text_list:
if len(mgt_text[i].split()) <= 1:
flag = 0
break
if flag:
res.append([a_human[i],
mgt_text_list[0][i],
mgt_text_list[1][i],
mgt_text_list[2][i],
mgt_text_list[3][i],
mgt_text_list[4][i],
mgt_text_list[5][i]])
data_new = {
'train': {
'text': [],
'label': [],
},
'test': {
'text': [],
'label': [],
}
}
index_list = list(range(len(res)))
random.seed(0)
random.shuffle(index_list)
total_num = len(res)
for i in tqdm.tqdm(range(total_num), desc="parsing data"):
if i < total_num * 0.8:
data_partition = 'train'
else:
data_partition = 'test'
for j in range(0, 7):
data_new[data_partition]['text'].append(
process_spaces(res[index_list[i]][j]))
data_new[data_partition]['label'].append(j)
return data_new