Implement `PROST` task

lm_eval/tasks/__init__.py

@@ -21,6 +21,7 @@ from . import arithmetic
 from . import lambada
 from . import race
 from . import piqa
+from . import prost
 from . import triviaqa
 from . import pubmedqa
 from . import sciq
@@ -101,6 +102,7 @@ TASK_REGISTRY = {
     # "cbt-ne": cbt.CBTNE, # disabled pending context length fix
 
     "piqa": piqa.PiQA,
+    "prost": prost.PROST,
 
     # Science related
     "pubmedqa" : pubmedqa.Pubmed_QA,

lm_eval/tasks/prost.py

+"""
+PROST: Physical Reasoning about Objects Through Space and Time
+https://arxiv.org/pdf/2106.03634.pdf
+
+NOTE: PROST is limited to the zero-shot setting to adhere to authors' intentions
+as discussed in section 7 of the paper: "We hope that the community will use
+this dataset in the intended way: in a zero-shot setting to probe models which
+have been trained on data not specifically collected to succeed on PROST."
+
+# TODO: Update citation when it is made available at https://github.com/nala-cub/prost.
+@misc{arocaouellette2021prost,
+      title={PROST: Physical Reasoning of Objects through Space and Time}, 
+      author={Stéphane Aroca-Ouellette and Cory Paik and Alessandro Roncone and Katharina Kann},
+      year={2021},
+      eprint={2106.03634},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+from lm_eval.base import MultipleChoiceTask
+from . common import HFTask
+
+
+class PROST(HFTask, MultipleChoiceTask):
+    VERSION = 0
+    DATASET_PATH = "corypaik/prost"
+    DATASET_NAME = None
+
+    def has_training_docs(self):
+        return False
+
+    def has_validation_docs(self):
+        return False
+
+    def has_test_docs(self):
+        return True
+
+    def fewshot_description(self):
+        # TODO: figure out fewshot description
+        return ""
+
+    def fewshot_context(self, doc, num_fewshot, provide_description, rnd):
+        assert num_fewshot == 0, 'PROST is designed to probe models in a zero-shot fashion only.'
+        return super().fewshot_context(doc, num_fewshot, provide_description, rnd)
+
+    def _convert_standard(self, doc):
+        out_doc = {
+            "query": f"{doc['context']}\nQuestion: {doc['ex_question']}\nAnswer:",
+            "choices": [doc['A'], doc['B'], doc['C'], doc['D']],
+            "gold": doc['label'],
+        }
+        return out_doc
+
+    def doc_to_text(self, doc):
+        return doc["query"]