Merge pull request EleutherAI#497 from ollmer/mmlu_fix

MMLU task fix

lm_eval/tasks/hendrycks_test.py

@@ -14,7 +14,6 @@
 """
 from lm_eval.base import MultipleChoiceTask
 
-
 _CITATION = """
 @article{hendryckstest2021,
     title={Measuring Massive Multitask Language Understanding},
@@ -103,16 +102,16 @@ def __init__(self):
 
 
 class GeneralHendrycksTest(MultipleChoiceTask):
-    VERSION = 0
-    DATASET_PATH = "hendrycks_test"
+    VERSION = 1
+    DATASET_PATH = "cais/mmlu"
     DATASET_NAME = None
 
     def __init__(self, subject):
         self.DATASET_NAME = subject
         super().__init__()
 
     def has_training_docs(self):
-        return False
+        return True
 
     def has_validation_docs(self):
         return True
@@ -126,41 +125,50 @@ def validation_docs(self):
     def test_docs(self):
         return map(self._process_doc, self.dataset["test"])
 
+    def _format_subject(self, subject):
+        words = subject.split("_")
+        return " ".join(words)
+
+    def fewshot_context(self, doc, num_fewshot, **kwargs):
+        subject = self.DATASET_NAME
+        description = f"The following are multiple choice questions (with answers) about {self._format_subject(subject)}."
+        kwargs["description"] = description
+        return super().fewshot_context(doc=doc, num_fewshot=num_fewshot, **kwargs)
+
     def _process_doc(self, doc):
         def format_example(doc, keys):
             """
-            Question: <prompt>
-            Choices:
+            <prompt>
             A. <choice1>
             B. <choice2>
             C. <choice3>
             D. <choice4>
             Answer:
             """
-            prompt = "Question: " + doc["question"] + "\nChoices:\n"
-            prompt += "".join(
+
+            question = doc["question"].strip()
+            choices = "".join(
                 [f"{key}. {choice}\n" for key, choice in zip(keys, doc["choices"])]
             )
-            prompt += "Answer:"
+            prompt = f"{question}\n{choices}Answer:"
             return prompt
 
         keys = ["A", "B", "C", "D"]
         return {
             "query": format_example(doc, keys),
-            "choices": doc["choices"],
-            "gold": keys.index(doc["answer"])
-            if isinstance(doc["answer"], str)
-            else doc["answer"],
+            "choices": keys,
+            "gold": doc["answer"],
         }
 
     def fewshot_examples(self, k, rnd):
         # fewshot_examples is not just sampling from train_docs because dev is
         # in the same distribution as val/test but auxiliary_train isn't
-
         if self._fewshot_docs is None:
             self._fewshot_docs = list(map(self._process_doc, self.dataset["dev"]))
 
-        return rnd.sample(list(self._fewshot_docs), k)
+        # use the unchanged order of the dev set without sampling,
+        # just as in the original code https://github.com/hendrycks/test/blob/master/evaluate.py#L28
+        return self._fewshot_docs[:k]
 
     def doc_to_text(self, doc):
         return doc["query"]