diff --git a/captum/attr/_core/llm_attr.py b/captum/attr/_core/llm_attr.py
index 737ac5c4be..f90492e39d 100644
--- a/captum/attr/_core/llm_attr.py
+++ b/captum/attr/_core/llm_attr.py
@@ -289,9 +289,12 @@ def _forward_func(
         # 1st element is the total prob, rest are the target tokens
         # add a leading dim for batch even we only support single instance for now
         if self.include_per_token_attr:
-            target_log_probs = torch.stack(
-                [total_log_prob, *log_prob_list], dim=0  # type: ignore
-            ).unsqueeze(0)
+            try:
+                target_log_probs = torch.stack(
+                    [total_log_prob, *log_prob_list], dim=0  # type: ignore
+                ).unsqueeze(0)
+            except TypeError:
+                raise TypeError("Try using the skip_bos argument.")
         else:
             target_log_probs = total_log_prob  # type: ignore
         target_probs = torch.exp(target_log_probs)
@@ -325,6 +328,10 @@ def attribute(
         inp: InterpretableInput,
         target: Union[str, torch.Tensor, None] = None,
         num_trials: int = 1,
+        skip_bos: bool = True,
+        # pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
+        #  `typing.Dict[<key type>, <value type>]` to avoid runtime subscripting
+        #  errors.
         gen_args: Optional[Dict[str, Any]] = None,
         use_cached_outputs: bool = True,
         # internal callback hook can be used for logging
@@ -375,8 +382,11 @@ def attribute(
             assert gen_args is None, "gen_args must be None when target is given"
 
             if type(target) is str:
-                # exclude sos
-                target_tokens = self.tokenizer.encode(target)[1:]
+                # exclude sos / bos
+                if skip_bos:
+                    target_tokens = self.tokenizer.encode(target)[1:]
+                else:
+                    target_tokens = self.tokenizer.encode(target)
                 target_tokens = torch.tensor(target_tokens)
             elif type(target) is torch.Tensor:
                 target_tokens = target