Update docs on LM.loglikelihood_rolling abstract method (#1532)

lm_eval/api/model.py

@@ -54,7 +54,7 @@ class LM(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def loglikelihood_rolling(self, requests) -> List[Tuple[float, bool]]:
+    def loglikelihood_rolling(self, requests) -> List[Tuple[float]]:
         """Compute full log-likelihood of a string, with no truncation, for perplexity computation
         - We will use the full max context length of the model.
         - For inputs that exceed the max context length, we divide the tokenized string into chunks of up to
@@ -83,15 +83,13 @@ class LM(abc.ABC):
             2. For the last pair, we provide the full context, but only score the last two tokens
 
         :param requests: list[Instance]
-            A list of Instance objects with property `args` which returns a tuple (context, continuation).
+            A list of Instance objects with property `args` which returns a tuple (context,).
             string: str
-                String for which we are computing per-token loglikelihood
-        :return: list[tuple[float, bool]]
-            A list of pairs (logprob, isgreedy)
+                String for which we are computing overall loglikelihood
+        :return: list[tuple[float]]
+            A list of tuples (logprob,)
             logprob: float
-                The log probability of `continuation`
-            isgreedy:
-                Whether `continuation` would be generated by greedy sampling from `context`
+                The log probability of `context` conditioned on the EOT token.
         """
         pass