Spaces:
Running
Running
File size: 11,634 Bytes
9df4cc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 |
import openai
import time
import json
import argparse
import csv
# START: COPIED FROM <https://github.com/RUCAIBox/HaluEval.git
openai.api_key = 'sk-'
def get_qa_res(knowledge, question, answer, instruction):
if isinstance(instruction, str):
message = [
{"role": "user", "content": instruction +
"\n\n#Knowledge#: " + knowledge +
"\n#Question#: " + question +
"\n#Right Answer#: " + answer +
"\n#Hallucinated Answer#: "}
]
elif isinstance(instruction, list):
mes = [{"role": "user",
"content": "You are now a mature hallucination generator. Please generate hallucinated answer for the following question. You can use any method you have learned that is suitable for the given question." +
"\n\n#Knowledge#: " + knowledge +
"\n#Question#: " + question +
"\n#Right Answer#: " + answer +
"\n#Hallucinated Answer#: "}]
message = instruction + mes
else:
raise TypeError("The instruction must be str or list!")
while True:
try:
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=message,
temperature=1,
max_tokens=256,
top_p=1
)
break
except openai.error.RateLimitError:
print('openai.error.RateLimitError\nRetrying...')
time.sleep(60)
except openai.error.ServiceUnavailableError:
print('openai.error.ServiceUnavailableError\nRetrying...')
time.sleep(20)
except openai.error.Timeout:
print('openai.error.Timeout\nRetrying...')
time.sleep(20)
except openai.error.APIError:
print('openai.error.APIError\nRetrying...')
time.sleep(20)
except openai.error.APIConnectionError:
print('openai.error.APIConnectionError\nRetrying...')
time.sleep(20)
# print(res['choices'][0]['message']['content'])
return res['choices'][0]['message']['content']
def get_dialogue_res(knowledge, dialog, response, instruction):
if isinstance(instruction, str):
message = [
{"role": "user", "content": instruction +
"\n\n#Knowledge#: " + knowledge +
"\n#Dialogue History#: " + dialog +
"\n#True Response#: " + response +
"\n#Hallucinated Response#: "}
]
elif isinstance(instruction, list):
mes = [{"role": "user",
"content": "You are now a mature hallucination generator. Please generate hallucinated response for the following dialogue. You can use any method you have learned that is suitable for the given dialogue history." +
"\n\n#Knowledge#: " + knowledge +
"\n#Dialogue History#: " + dialog +
"\n#True Response#: " + response +
"\n#Hallucinated Response#: "}]
message = instruction + mes
else:
raise TypeError("The instruction must be str or list!")
while True:
try:
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=message,
temperature=1,
max_tokens=256,
top_p=1
)
break
except openai.error.RateLimitError:
print('openai.error.RateLimitError\nRetrying...')
time.sleep(60)
except openai.error.ServiceUnavailableError:
print('openai.error.ServiceUnavailableError\nRetrying...')
time.sleep(20)
except openai.error.Timeout:
print('openai.error.Timeout\nRetrying...')
time.sleep(20)
except openai.error.APIError:
print('openai.error.APIError\nRetrying...')
time.sleep(20)
except openai.error.APIConnectionError:
print('openai.error.APIConnectionError\nRetrying...')
time.sleep(20)
# print(res['choices'][0]['message']['content'])
return res['choices'][0]['message']['content']
def get_summarization_res(text, summary, instruction):
if isinstance(instruction, str):
message = [
{"role": "user", "content": instruction +
"\n\n#Document#: " + text +
"\n#Right Summary#: " + summary +
"\n#Hallucinated Summary#: "}
]
elif isinstance(instruction, list):
mes = [{"role": "user",
"content": "You are now a mature hallucination generator. Please generate hallucinated summary for the following document. You can use any method you have learned that is suitable for the given document. #Hallucinated Summary# must not be longer than #Right Summary#." +
"\n\n#Document#: " + text +
"\n#Right Summary#: " + summary +
"\n#Hallucinated Summary#: "}]
message = instruction + mes
else:
raise TypeError("The instruction must be str or list!")
while True:
try:
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=message,
temperature=1,
max_tokens=256,
top_p=1
)
break
except openai.error.RateLimitError:
print('openai.error.RateLimitError\nRetrying...')
time.sleep(60)
except openai.error.ServiceUnavailableError:
print('openai.error.ServiceUnavailableError\nRetrying...')
time.sleep(20)
except openai.error.Timeout:
print('openai.error.Timeout\nRetrying...')
time.sleep(20)
except openai.error.APIError:
print('openai.error.APIError\nRetrying...')
time.sleep(20)
except openai.error.APIConnectionError:
print('openai.error.APIConnectionError\nRetrying...')
time.sleep(20)
# print(res['choices'][0]['message']['content'])
return res['choices'][0]['message']['content']
def generate_qa_dataset(seed_data, instruction, output_path):
with open(seed_data, 'r', encoding="utf-8") as f:
text = json.load(f)
for i in range(10000):
question = text[i]['question']
answer = text[i]['answer']
supporting_facts = text[i]['supporting_facts']
context = text[i]['context']
knowledge = ""
for fact in supporting_facts:
for para in context:
if para[0] == fact[0]:
if fact[1] < len(para[1]):
knowledge = knowledge + para[1][fact[1]]
ans = get_qa_res(knowledge, question, answer, instruction)
data = {"knowledge": knowledge, "question": question, "right_answer": answer, "hallucinated_answer": ans}
dump_jsonl(data, output_path, append=True)
print(" sample {} completed!".format(i))
def generate_dialogue_dataset(seed_data, instruction, output_path):
SENDER = {"user": "[Human]", "assistant": "[Assistant]"}
with open(seed_data, 'r', encoding="utf-8") as f:
i = 0
data = csv.DictReader(f)
for r in data:
if i >= 10000:
break
r = eval(r['Messages'])
dialog = ""
knowledge = ""
response = ""
k = 0
d = 0
for message in r:
if "message" in message:
if k > 1 and message['sender'] == "assistant":
response = message['message']
break
if d > 3 and message['sender'] == "assistant":
response = message['message']
break
else:
dialog = dialog + (SENDER[message['sender']] + ": " + message['message']) + " "
d = d + 1
if "metadata" in message:
if "path" in message['metadata']:
knowledge = knowledge + message['metadata']['path'][2]
k = k + 1
if knowledge == "" or dialog == "" or response == "":
continue
res = get_dialogue_res(knowledge, dialog, response, instruction)
data = {"knowledge": knowledge, "dialogue_history": dialog, "right_response": response, "hallucinated_response": res}
dump_jsonl(data, output_path, append=True)
i = i + 1
print("sample {} completed!".format(i))
def generate_summarization_dataset(seed_data, instruction, output_path):
with open(seed_data, 'r', encoding="utf-8") as f:
data = f.readlines()
text = [json.loads(d) for d in data]
for i in range(10000):
document = text[i]["document"]
summary = text[i]["summary"]
sum = get_summarization_res(document, summary, instruction)
data = {"document": document, "right_summary": summary, "hallucinated_summary": sum}
dump_jsonl(data, output_path, append=True)
print("sample {} completed!".format(i))
def dump_jsonl(data, output_path, append=False):
"""
Write list of objects to a JSON lines file.
"""
mode = 'a+' if append else 'w'
with open(output_path, mode, encoding='utf-8') as f:
json_record = json.dumps(data, ensure_ascii=False)
f.write(json_record + '\n')
# END: COPIED FROM < https://github.com/RUCAIBox/HaluEval.git >
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Hallucination Generation")
parser.add_argument("--seed_data", default="hotpot_train_v1.1.json", help="the original dataset file")
parser.add_argument("--task", default="qa", help="qa, dialogue, or summarization")
parser.add_argument("--strategy",default="one-turn", help="one-turn or multi-turn")
args = parser.parse_args()
seed_data = args.seed_data
if args.strategy == "one-turn":
instruction_file = "{}/{}_{}_instruction.txt".format(args.task, args.task, args.strategy)
f = open(instruction_file, 'r', encoding="utf-8")
instruction = f.read()
elif args.strategy == "multi-turn":
instruction_file = "{}/{}_{}_instruction.json".format(args.task, args.task, args.strategy)
with open(instruction_file, 'r', encoding="utf-8") as f:
lines = f.readlines()
instruction = [json.loads(line) for line in lines]
else:
raise ValueError("The strategy must be one-turn or multi-turn!")
output_path = "{}/{}_{}_data.json".format(args.task, args.task, args.strategy)
if args.task == "qa":
generate_qa_dataset(seed_data, instruction, output_path)
elif args.task == "dialogue":
generate_dialogue_dataset(seed_data, instruction, output_path)
elif args.task == "summarization":
generate_summarization_dataset(seed_data, instruction, output_path)
else:
raise ValueError("The task must be qa, dialogue, or summarization!")
|