生成上下文示例
我们如何为提示生成示例？
自生成上下文学习 (SG-ICL) 是一种使用大型语言模型 (LLM) 生成任务期间使用的示例的技术。这允许进行上下文学习，其中任务的示例在提示中提供。
我们可以使用 instructor 实现 SG-ICL，如下所示。
import instructor
from pydantic import BaseModel
from openai import OpenAI
from typing import Literal
n = 4 # num examples to generate per class
class GeneratedReview(BaseModel):
review: str
sentiment: Literal["positive", "negative"]
class SentimentPrediction(BaseModel):
sentiment: Literal["positive", "negative"]
client = instructor.from_openai(OpenAI())
def generate_sample(input_review, sentiment):
return client.chat.completions.create(
model="gpt-4o",
response_model=GeneratedReview,
messages=[
{
"role": "user",
"content": f"""
Generate a '{sentiment}' review similar to: {input_review}
Generated review:
""",
}
],
)
def predict_sentiment(input_review, in_context_samples):
return client.chat.completions.create(
model="gpt-4o",
response_model=SentimentPrediction,
messages=[
{
"role": "user",
"content": "".join(
[
f"Review: {sample.review}\nSentiment: {sample.sentiment}\n\n"
for sample in in_context_samples
]
)
+ f"Review: {input_review}\nSentiment:",
}
],
).sentiment
if __name__ == "__main__":
input_review = (
"This movie was a rollercoaster of emotions, keeping me engaged throughout."
)
# Generate in-context samples
samples = [
generate_sample(input_review, sentiment)
for sentiment in ('positive', 'negative')
for _ in range(n)
]
for sample in samples:
print(sample)
"""
review='This film was an enthralling experience from start to finish, leaving me captivated every moment.' sentiment='positive'
"""
"""
review='This film was an emotional journey that captivated me from start to finish.' sentiment='positive'
"""
"""
review='The film took me on an unforgettable journey, capturing my attention at every moment.' sentiment='positive'
"""
"""
review='This book was a riveting journey, capturing my attention from start to finish.' sentiment='positive'
"""
"""
review='The movie was a total letdown, failing to hold my interest from start to finish.' sentiment='negative'
"""
"""
review='This movie was a disjointed mess of emotions, leaving me confused throughout.' sentiment='negative'
"""
"""
review='The movie was an emotional rollercoaster, but it left me feeling more confused than engaged.' sentiment='negative'
"""
"""
review='This movie was a monotonous ride, failing to engage me at any point.' sentiment='negative'
"""
"""
review='This film was an emotional journey, captivating me from start to finish.' sentiment='positive'
"""
"""
review='This film captivated me from start to finish with its thrilling plot and emotional depth.' sentiment='positive'
"""
"""
review='This movie was a breathtaking journey, capturing my attention from start to finish.' sentiment='positive'
"""
"""
review='This movie was a chaotic mess of emotions, losing me at every turn.' sentiment='negative'
"""
"""
review='This movie was a confusing mess, leaving me disengaged throughout.' sentiment='negative'
"""
"""
review='This movie was a chore to sit through, leaving me bored most of the time.' sentiment='negative'
"""
"""
review='This movie was a mishmash of confusing scenes, leaving me frustrated throughout.' sentiment='negative'
"""
# Predict sentiment
print(predict_sentiment(input_review, samples))
#> positive