Add multiple annotators to Omni-MATH and rename shared modules

src/helm/benchmark/annotation/model_as_judge.py

@@ -13,7 +13,7 @@ def __init__(self, response_text: str, **kwargs):
 
 
 @dataclass
-class _AnnotatorModelInfo:
+class AnnotatorModelInfo:
     model_name: str
     model_deployment: str
 
@@ -26,9 +26,9 @@ def score_with_reasoning_with_gpt_and_llama(
 
     Score using GPT-4o and Llama 3.1 for safety scenarios in HELM Safety."""
     # TODO: Make this configurable
-    SHORT_NAME_TO_MODEL_INFO: Dict[str, _AnnotatorModelInfo] = {
-        "gpt": _AnnotatorModelInfo(model_name="openai/gpt-4o-2024-05-13", model_deployment="openai/gpt-4o-2024-05-13"),
-        "llama": _AnnotatorModelInfo(
+    SHORT_NAME_TO_MODEL_INFO: Dict[str, AnnotatorModelInfo] = {
+        "gpt": AnnotatorModelInfo(model_name="openai/gpt-4o-2024-05-13", model_deployment="openai/gpt-4o-2024-05-13"),
+        "llama": AnnotatorModelInfo(
             model_name="meta/llama-3.1-405b-instruct-turbo", model_deployment="together/llama-3.1-405b-instruct-turbo"
         ),
     }

src/helm/benchmark/annotation/omni_math_annotator.py

@@ -1,8 +1,9 @@
-from typing import Any
+from typing import Any, Dict
 from importlib.resources import files
 
 from helm.benchmark.adaptation.request_state import RequestState
 from helm.benchmark.annotation.annotator import Annotator
+from helm.benchmark.annotation.model_as_judge import AnnotatorModelInfo
 from helm.clients.auto_client import AutoClient
 from helm.common.request import Request
 
@@ -51,28 +52,52 @@ def annotate(self, request_state: RequestState) -> Any:
                 "justification": "The model output is empty.",
             }
 
-        annotator_request = Request(
-            model="openai/gpt-4o-2024-05-13",
-            model_deployment="openai/gpt-4o-2024-05-13",
-            prompt=annotator_prompt,
-            temperature=0.0,
-            max_tokens=1000,
-        )
-        annotator_response = self._auto_client.make_request(annotator_request)
-        if not annotator_response.success:
-            raise Exception(f"Annotation request failed: {annotator_response.error}")
-        assert len(annotator_response.completions) == 1
-        annotator_response_text = annotator_response.completions[0].text
+        SHORT_NAME_TO_MODEL_INFO: Dict[str, AnnotatorModelInfo] = {
+            "gpt": AnnotatorModelInfo(
+                model_name="openai/gpt-4o-2024-05-13", model_deployment="openai/gpt-4o-2024-05-13"
+            ),
+            "llama": AnnotatorModelInfo(
+                model_name="meta/llama-3.1-405b-instruct-turbo",
+                model_deployment="together/llama-3.1-405b-instruct-turbo",
+            ),
+            "claude": AnnotatorModelInfo(
+                model_name="anthropic/claude-3-5-sonnet-20241022",
+                model_deployment="anthropic/claude-3-5-sonnet-20241022",
+            ),
+        }
+        all_student_final_answers = []
+        all_equivalence_judgements = []
+        all_justifications = []
+        for annotator_model in SHORT_NAME_TO_MODEL_INFO:
+            annotator_model_info = SHORT_NAME_TO_MODEL_INFO[annotator_model]
+            annotator_request = Request(
+                model=annotator_model_info.model_name,
+                model_deployment=annotator_model_info.model_deployment,
+                prompt=annotator_prompt,
+                temperature=0.0,
+                max_tokens=1000,
+            )
+            annotator_response = self._auto_client.make_request(annotator_request)
+            if not annotator_response.success:
+                raise Exception(f"Annotation request failed: {annotator_response.error}")
+            assert len(annotator_response.completions) == 1
+            annotator_response_text = annotator_response.completions[0].text
+
+            info = parse_report(annotator_response_text)
 
-        info = parse_report(annotator_response_text)
+            equivalence_judgement = info.get("Equivalence Judgement", "")
+            student_final_answer = info.get("Student Final Answer", "")
+            justification = info.get("Justification", "").strip().removesuffix("=== report over ===").strip()
+            if equivalence_judgement == "":
+                continue  # skip this annotator if there is no equivalence judgement parsed
 
-        equivalence_judgement = info.get("Equivalence Judgement", "")
-        student_final_answer = info.get("Student Final Answer", "")
-        justification = info.get("Justification", "").strip().removesuffix("=== report over ===").strip()
+            all_student_final_answers.append(student_final_answer)
+            all_equivalence_judgements.append(equivalence_judgement)
+            all_justifications.append(justification)
 
         return {
             "prompt_text": annotator_prompt,
-            "student_final_answer": student_final_answer,
-            "equivalence_judgement": equivalence_judgement,
-            "justification": justification,
+            "student_final_answer": all_student_final_answers,
+            "equivalence_judgement": all_equivalence_judgements,
+            "justification": all_justifications,
         }

src/helm/benchmark/annotation/wildbench_annotator.py

@@ -5,7 +5,7 @@
 
 from helm.benchmark.adaptation.request_state import RequestState
 from helm.benchmark.annotation.annotator import Annotator
-from helm.benchmark.annotation.model_as_judge import _AnnotatorModelInfo
+from helm.benchmark.annotation.model_as_judge import AnnotatorModelInfo
 from helm.clients.auto_client import AutoClient
 from helm.common.request import Request
 
@@ -46,15 +46,15 @@ def annotate(self, request_state: RequestState) -> Any:
             .replace("{$checklist}", "\n".join(request_state.instance.extra_data["checklist"]))
         )
 
-        SHORT_NAME_TO_MODEL_INFO: Dict[str, _AnnotatorModelInfo] = {
-            "gpt": _AnnotatorModelInfo(
+        SHORT_NAME_TO_MODEL_INFO: Dict[str, AnnotatorModelInfo] = {
+            "gpt": AnnotatorModelInfo(
                 model_name="openai/gpt-4o-2024-05-13", model_deployment="openai/gpt-4o-2024-05-13"
             ),
-            "llama": _AnnotatorModelInfo(
+            "llama": AnnotatorModelInfo(
                 model_name="meta/llama-3.1-405b-instruct-turbo",
                 model_deployment="together/llama-3.1-405b-instruct-turbo",
             ),
-            "claude": _AnnotatorModelInfo(
+            "claude": AnnotatorModelInfo(
                 model_name="anthropic/claude-3-5-sonnet-20241022",
                 model_deployment="anthropic/claude-3-5-sonnet-20241022",
             ),

src/helm/benchmark/metrics/omni_math_metrics.py

@@ -19,7 +19,11 @@ def evaluate_generation(
         eval_cache_path: str,
     ) -> List[Stat]:
         assert request_state.annotations
-        score = request_state.annotations["omni_math"]["equivalence_judgement"].strip().upper() == "TRUE"
+        all_judgements = request_state.annotations["omni_math"]["equivalence_judgement"]
+        if len(all_judgements) == 0:
+            raise ValueError("Could not compute Omni-MATH accuracy because all annotators failed.")
+        judgement_bools = [judgement.strip().upper() == "TRUE" for judgement in all_judgements]
+        score = sum(judgement_bools) / len(judgement_bools)
         return [
             Stat(MetricName("omni_math_accuracy")).add(score),
         ]