dosubot[bot]
commented
2 months ago To address the issues with the Answer Relevancy feature generating the same questions every time and causing inconsistent scoring, you can consider the following steps:
-
Ensure Randomness in Question Generation: Modify the
QUESTION_GENprompt to include a variety of examples and instructions that encourage diverse question generation. -
Check Embedding Consistency: Ensure that the embeddings used for calculating similarity are consistent and correctly implemented.
-
Adjust Strictness Parameter: The
strictnessparameter controls the number of questions generated per answer. Ensure it is set to an appropriate value (e.g., 3 by default).
Here is a modified version of the relevant parts of the code to address these issues:
QUESTION_GEN = Prompt(
name="question_generation",
instruction="""Generate a diverse set of questions for the given answer and identify if the answer is noncommittal. Give noncommittal as 1 if the answer is noncommittal and 0 if the answer is committal. A noncommittal answer is one that is evasive, vague, or ambiguous. For example, "I don't know" or "I'm not sure" are noncommittal answers. Ensure the questions are varied and cover different aspects of the context.""",
output_format_instruction=_output_instructions,
examples=[
{
"answer": """Albert Einstein was born in Germany.""",
"context": """Albert Einstein was a German-born theoretical physicist who is widely held to be one of the greatest and most influential scientists of all time""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "Where was Albert Einstein born?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """It can change its skin color based on the temperature of its environment.""",
"context": """A recent scientific study has discovered a new species of frog in the Amazon rainforest that has the unique ability to change its skin color based on the temperature of its environment.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What unique ability does the newly discovered species of frog have?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """Everest""",
"context": """The tallest mountain on Earth, measured from sea level, is a renowned peak located in the Himalayas.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What is the tallest mountain on Earth?",
"noncommittal": 0,
}
).dict(),
},
{
"answer": """I don't know about the groundbreaking feature of the smartphone invented in 2023 as I am unaware of information beyond 2022.""",
"context": """In 2023, a groundbreaking invention was announced: a smartphone with a battery life of one month, revolutionizing the way people use mobile technology.""",
"output": AnswerRelevanceClassification.parse_obj(
{
"question": "What was the groundbreaking feature of the smartphone invented in 2023?",
"noncommittal": 1,
}
).dict(),
},
],
input_keys=["answer", "context"],
output_key="output",
output_type="json",
)
@dataclass
class AnswerRelevancy(MetricWithLLM, MetricWithEmbeddings):
name: str = "answer_relevancy"
evaluation_mode: EvaluationMode = EvaluationMode.qac
question_generation: Prompt = field(default_factory=lambda: QUESTION_GEN)
strictness: int = 3 # Adjust this value as needed
def calculate_similarity(
self: t.Self, question: str, generated_questions: list[str]
):
assert self.embeddings is not None
question_vec = np.asarray(self.embeddings.embed_query(question)).reshape(1, -1)
gen_question_vec = np.asarray(
self.embeddings.embed_documents(generated_questions)
).reshape(len(generated_questions), -1)
norm = np.linalg.norm(gen_question_vec, axis=1) * np.linalg.norm(
question_vec, axis=1
)
return (
np.dot(gen_question_vec, question_vec.T).reshape(
-1,
)
/ norm
)
def _calculate_score(
self, answers: t.Sequence[AnswerRelevanceClassification], row: t.Dict
) -> float:
question = row["question"]
gen_questions = [answer.question for answer in answers]
committal = np.any([answer.noncommittal for answer in answers])
if all(q == "" for q in gen_questions):
logger.warning(
"Invalid JSON response. Expected dictionary with key 'question'"
)
score = np.nan
else:
cosine_sim = self.calculate_similarity(question, gen_questions)
score = cosine_sim.mean() * int(not committal)
return score
def _create_question_gen_prompt(self, row: t.Dict) -> PromptValue:
ans, ctx = row["answer"], row["contexts"]
return self.question_generation.format(answer=ans, context="\n".join(ctx))
async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
assert self.llm is not None, "LLM is not set"
prompt = self._create_question_gen_prompt(row)
result = await self.llm.generate(
prompt,
n=self.strictness,
callbacks=callbacks,
)
answers = [
await _output_parser.aparse(result.text, prompt, self.llm)
for result in result.generations[0]
]
if any(answer is None for answer in answers):
return np.nan
answers = [answer for answer in answers if answer is not None]
return self._calculate_score(answers, row)These changes should help in generating more diverse questions and ensuring consistent scoring. Additionally, the Answer Relevancy metric is defined as the mean cosine similarity of the original question to a number of artificial questions generated based on the answer. This ensures that if the generated answer accurately addresses the initial question, the LLM should be able to generate questions from the answer that align with the original question [1][2].
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knpunk
@dosu I've gone through the library and first of all, the prompt needs to have something that instructs to go through the context, rather than just providing in few shot. Second, I streamed the questions through your help but all three questions are exactly same, and that is happening for every row. Maybe that is why the score is inconsistent, help me fix it please.