Medhelm judges #3484
Medhelm judges #3484
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| hlog("WARNING: Annotator skipped sending requests because the model response was empty") | ||
| return { | ||
| "prompt_text": None, | ||
| "empty_output_equivalence_judgement": False, |
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Probably want to rename this key.
| prompt_template: str, | ||
| annotation_criteria: Dict[str, Set[str]], | ||
| annotator_models: Dict[str, AnnotatorModelInfo], | ||
| preprocessor: Optional[Callable[[str], str]] = None, |
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nit: output_preprocessor
| # Attempt to fix incomplete JSON by adding a closing brace | ||
| annotator_output = annotator_output + "}" | ||
| try: | ||
| annotator_criteria = json.loads(annotator_output) |
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annotation_criteria -> annotation?
| """ | ||
| self._auto_client = auto_client | ||
| self._prompt_template = prompt_template | ||
| self._annotation_criteria = annotation_criteria |
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nit: annotation_schema or annotation_format
| metric_args = { | ||
| "task": "mtsamples_replicate", | ||
| "device": get_torch_device_name(), | ||
| "bertscore_model": "distilbert-base-uncased", | ||
| "rescale_with_baseline": False, | ||
| } | ||
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| metric_specs = get_summarization_metric_specs(metric_args) + [ | ||
| MetricSpec(class_name="helm.benchmark.metrics.mtsamples_replicate_metrics.MTSamplesReplicateMetric", args={}) |
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optional nit: you can omit args={} in most specs.
| metric_service: MetricService, | ||
| eval_cache_path: str, | ||
| ) -> List[Stat]: | ||
| assert request_state.annotations |
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Is there some way that the commonality between these metrics could be refactored out? See AnnotationLikertScaleMetric here for an example that you could follow.
| "clarity": {"score", "explanation"}, | ||
| } | ||
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| ANNOTATOR_MODELS: Dict[str, AnnotatorModelInfo] = { |
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If you need to switch between different predefined deployments in different environments, you could make an environment enum (e.g. "shc", "medical") and then do one of the below:
- Add a parameter to the run spec function that specifies an environment enum value
- Read in a shell environment variable to get the environment enum value
MedHELM contains several open-ended benchmarks where the current main metric is BertScore. While this metric is widely accepted in the literature, we want to make our evaluation metrics more robust.
To do so, we propose the use of LLM as a judge for the open-ended benchmarks, where we use standaridized evaluation criteria for each scenario and judges from multiple vendors the reduce bias.
Default judges:
*model_deployment="stanfordhealthcare/gpt-4o-2024-05-13"
Adding @suhana13 @aunell @HennyJie for FYI.