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# Check if the predicted answer matches the ground truth
def check_answer(prediction, ground_truth):
prediction = prediction.lower()
if type(ground_truth) is not list:
ground_truth = [ground_truth]
labels = []
for instance in ground_truth:
flag = True
if isinstance(instance, list):
flag = False
instance = [i.lower() for i in instance]
for i in instance:
if i in prediction:
flag = True
break
else:
instance = instance.lower()
if instance not in prediction:
flag = False
labels.append(int(flag))
return labels
# Evaluate if the result is correct (non-zero indicates correctness)
def get_evaluation(results):
return 0 not in results
# Generate prediction based on query, documents, and model
def predict(query, ground_truth, docs, model, instruction, temperature):
'''
label: 0 for positive, 1 for negative, -1 for not enough information
'''
system_message = (
'You are an accurate and reliable AI assistant that can answer questions with the help of external documents. '
'Please note that external documents may contain noisy or factually incorrect information. If the information '
'in the document contains the correct answer, you will give an accurate answer. If the information in the '
'document does not contain the answer, you will generate "I can not answer the question because of the insufficient information in documents." '
'If there are inconsistencies with the facts in some of the documents, please generate the response: "There are factual errors in the provided documents and provide the correct answer."'
)
if len(docs) == 0:
text = instruction.format(QUERY=query, DOCS='')
prediction = model.generate(text, temperature)
else:
docs = '\n'.join(docs)
text = instruction.format(QUERY=query, DOCS=docs)
prediction = model.generate(text, temperature, system_message)
# Check if the prediction contains the 'insufficient information' phrase
if 'insufficient information' in prediction:
labels = [-1]
else:
labels = check_answer(prediction, ground_truth)
# Check for factual errors in the prediction
fact_label = 0
if 'factual errors' in prediction:
fact_label = 1
return labels, prediction, fact_label
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