diff --git a/vlm_eval/models/instructblip.py b/vlm_eval/models/instructblip.py
index e84f5c4..8fa3c4f 100644
--- a/vlm_eval/models/instructblip.py
+++ b/vlm_eval/models/instructblip.py
@@ -15,7 +15,12 @@
 from vlm_eval.util.interfaces import VLM, ImageProcessor, Tokenizer
 
 # Define InstructBLIP Mapping from Model ID --> HF Hub Path
-INSTRUCTBLIP_MODELS = {"instructblip-vicuna-7b": "Salesforce/instructblip-vicuna-7b"}
+INSTRUCTBLIP_MODELS = {
+    # fmt: off
+    "instructblip-vicuna-7b": "Salesforce/instructblip-vicuna-7b",
+    "instructblip-vicuna-13b": "Salesforce/instructblip-vicuna-13b",
+    # fmt: on
+}
 
 
 class InstructBLIP(VLM):
@@ -57,9 +62,10 @@ def __init__(
         #     "temperature": 1,
         # }
 
-        # For computing likelihoods --> get tokens corresponding to "true", "false" and "yes", "no"
+        # For computing likelihoods --> get tokens corresponding to "true", "false" and "yes", "no" for T/F questions
+        # and also the lower-case alphabet for MC questions
         self.string2idx = {}
-        for trigger_string in ["true", "false", "yes", "no"]:
+        for trigger_string in ["true", "false", "yes", "no"] + [f"{chr(ord('a') + idx)}" for idx in range(26)]:
             token_idx_list = self.text_img_processor.tokenizer.encode(trigger_string, add_special_tokens=False)
             assert len(token_idx_list) == 1, f'String "{trigger_string}" is tokenized as more than one token!'
             self.string2idx[trigger_string] = token_idx_list[0]
@@ -93,6 +99,8 @@ def get_prompt_fn(self, dataset_family: str = "vqa-v2") -> Callable[[str], str]:
         bbox_refer_prompt_fn = self.get_bbox_refer_chat_prompt_fn()
         text_vqa_prompt_fn = self.get_vqa_chat_prompt_fn(uncertainty_aware=False, ocr_handling=True)
         captioning_prompt_fn = self.get_captioning_prompt_fn()
+        tally_qa_prompt_fn = self.get_mc_prompt_fn()
+        ai2d_prompt_fn = self.get_mc_prompt_fn()
 
         return {
             "vqa-v2": vqa_prompt_fn,
@@ -103,6 +111,7 @@ def get_prompt_fn(self, dataset_family: str = "vqa-v2") -> Callable[[str], str]:
             "pope": vqa_prompt_fn,
             "refcoco": bbox_refer_prompt_fn,
             "ocid-ref": bbox_refer_prompt_fn,
+            "ai2d": ai2d_prompt_fn,
             # Generic for GUI
             "captioning": captioning_prompt_fn,
             "bbox_pred": bbox_refer_prompt_fn,
@@ -154,6 +163,20 @@ def contrast_caption_prompt_fn(caption: str) -> str:
 
         return contrast_caption_prompt_fn
 
+    @staticmethod
+    def get_mc_prompt_fn() -> Callable[[str], str]:
+        """Generates the full reference prompt for a multiple-choice question-answer task."""
+
+        def mc_prompt_fn(question: str, choices: List[str]) -> str:
+            # Create Choice String
+            assert len(choices) <= 26, "Too many answer choices vs. possible letters in the alphabet!"
+            choice_str = "\n".join([f"{chr(ord('A') + idx)}. {choice}" for idx, choice in enumerate(choices)])
+            q_prompt = "{}\n{}".format(question, choice_str)
+            q_prompt += "\nAnswer with the option's letter from the given choices directly."
+            return q_prompt
+
+        return mc_prompt_fn
+
     @staticmethod
     def get_bbox_refer_chat_prompt_fn() -> Callable[[str], str]:
         """Generates the full reference prompt for a referring expression localization task."""