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feat: add aggregation_functions to llm_as_judge_405b_simpleqa (#1164)

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as title, to let scoring function llm_as_judge_405b_simpleqa output
aggregated_results.

We can leverage categorical_count to calculate the % of correctness as
eval benchmark metrics
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Botao Chen 2025-02-19 19:42:04 -08:00 • committed by GitHub
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7
llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_405b_simpleqa.py
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@ -5,7 +5,11 @@
# the root directory of this source tree.
from llama_stack.apis.common.type_system import NumberType
from llama_stack.apis.scoring_functions import LLMAsJudgeScoringFnParams, ScoringFn
from llama_stack.apis.scoring_functions import (
AggregationFunctionType,
LLMAsJudgeScoringFnParams,
ScoringFn,
)
GRADER_TEMPLATE = """
Your job is to look at a question, a gold target, and a predicted answer, and then assign a grade of either ["CORRECT", "INCORRECT", "NOT_ATTEMPTED"].
@ -87,5 +91,6 @@ llm_as_judge_405b_simpleqa = ScoringFn(
judge_model="meta-llama/Llama-3.1-405B-Instruct",
prompt_template=GRADER_TEMPLATE,
judge_score_regexes=[r"(A|B|C)"],
aggregation_functions=[AggregationFunctionType.categorical_count.value],
),
)