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shroom_classifier_v4_persona.py
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shroom_classifier_v4_persona.py
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# -*- coding: utf-8 -*-
from operator import itemgetter
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
class ShroomClassifier:
"""A classifier for the SHROOM validation and test datasets."""
PERSONA = {
"MT": "a translator concerned that the output is a good and accurate translation",
"DM": "a lexicographer concerned that the output accurately captures the meaning of the term between the '<define>' and '</define>' delimiters in the input",
"TS": "an editor concerned that the output is short and simple",
"PG": "an author concerned that the output is an accurate paraphrase that does not distort the meaning of the input",
}
TASK = {
"DM": "The given task is Definition Modeling, meaning that the goal of the language model is to generate a definition for a specific term in the input.",
"PG": "The given task is Paraphrase Generation, meaning that the goal of the language model is to generate a paraphrase of the input.",
"MT": "The given task is Machine Translation, meaning that the goal of the language model is to generate a natural language translation of the input.",
"TS": "The given task is Text Simplification, meaning that the goal of the language model is to generate a simplified version of the input.",
}
ANSWER_GENERATION_PROMPT = """A language model has generated an output from a given input for a specific task.
{task} You are {persona}. You will be given three inputs: input text, target text, and generated text.
You are asked to evaluate the generated text looking at the input text and the target text. Then, you need to decide whether the generated text is a hallucination or not.
There are two criteria for hallucination:
- If the generated text contains any nonsensical or factually incorrect information, it is a hallucination.
- If the generated text contains additional information that cannot be supported by the input text or the target text, it is a hallucination.
- Else, the generated text is not a hallucination.
Now, it is time to look at the inputs.
Input text: {src}
Target text: {tgt}
Generated text: {hyp}
Is the generated text a hallucination? Answer 'Hallucination' if the output is a hallucination, or 'Not Hallucination'
if it is not a hallucination. Only answer 'Hallucination' or 'Not Hallucination'.
Answer:
"""
def __init__(self, model_name="gpt-4", temperature=0.1):
"""
Initializes a classifier for the SemEval 2024 Task 6.
Parameters:
model_name: The name of the model to be used for classification (default "gpt-4").
temperature: The temperature parameter for the model (default 0.1).
"""
self.model_name = model_name
self.temperature = temperature
self.llm = self._llm(model_name, temperature)
self.chain = self._chain()
def _llm(self, model_name, temperature):
"""
Initializes a model for use in classification.
Parameters:
model_name: The name of the model to be used for classification.
temperature: The temperature parameter for the model.
Returns:
An LCEL model.
"""
if model_name in [
"gpt-4",
"gpt-3.5-turbo",
"gpt-4-1106-preview",
]:
return ChatOpenAI(model_name=model_name, temperature=temperature, request_timeout=100)
else:
raise Exception(f'Model {model_name} not supported')
def _chain(self):
"""
Creates an LCEL chain.
Returns:
An LCEL chain.
"""
return (
ChatPromptTemplate.from_template(self.ANSWER_GENERATION_PROMPT)
| self.llm
| StrOutputParser()
)
def classify(self, datapoint):
"""
Determines whether or not the output (hyp) is a hallucination, and
produces an estimate of the probability thatthe output is a hallucination.
Parameters:
datapoint: A datapoint from the SHROOM dataset.
Returns:
A dict containing a classification of the output based on the task, input, output and target.
"""
predictions = self.chain.batch([
{
"task": self.TASK[datapoint["task"]],
"persona": self.PERSONA[datapoint["task"]],
"src": datapoint["src"],
"tgt": datapoint["tgt"],
"hyp": datapoint["hyp"]
} for i in range(5) ])
weight = 1./float(len(predictions))
predicted_p = float(sum([ weight for prediction in predictions if prediction == 'Hallucination' ]))
if predicted_p > 0.5:
predicted = "Hallucination"
else:
predicted = "Not Hallucination"
if "id" in datapoint:
return { "id": datapoint["id"], "label": predicted, "p(Hallucination)": predicted_p }
else:
return { "label": predicted, "p(Hallucination)": predicted_p }