diff --git a/expo/data/dataset.py b/expo/data/dataset.py
index 28bd26d2e..8ad6f2854 100644
--- a/expo/data/dataset.py
+++ b/expo/data/dataset.py
@@ -20,12 +20,23 @@ USE_AG = """
 7. Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric.
 """
 
+TEXT_MODALITY = """
+7. You could use models from transformers library for this text dataset.
+8. Use gpu if available for faster training.
+"""
+
+IMAGE_MODALITY = """
+7. You could use models from torchvision library for this image dataset.
+8. Use gpu if available for faster training.
+"""
+
 STACKING = """
 7. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor.
 8. You could do some quick model prototyping to see which models work best and then use them in the ensemble. 
 """
 
-SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING}
+
+SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING, "text": TEXT_MODALITY, "image": IMAGE_MODALITY}
 
 DI_INSTRUCTION = """
 ## Attention
diff --git a/expo/run_experiment.py b/expo/run_experiment.py
index be028c47e..8dd66577c 100644
--- a/expo/run_experiment.py
+++ b/expo/run_experiment.py
@@ -3,10 +3,10 @@ import asyncio
 
 from expo.experimenter.aug import AugExperimenter
 from expo.experimenter.autogluon import GluonExperimenter
+from expo.experimenter.autosklearn import AutoSklearnExperimenter
 from expo.experimenter.custom import CustomExperimenter
 from expo.experimenter.experimenter import Experimenter
 from expo.experimenter.mcts import MCTSExperimenter
-from expo.experimenter.autosklearn import AutoSklearnExperimenter
 
 
 def get_args():
@@ -43,7 +43,7 @@ def get_di_args(parser):
     parser.add_argument("--reflection", dest="reflection", action="store_true")
     parser.add_argument("--no_reflection", dest="reflection", action="store_false")
     parser.add_argument("--num_experiments", type=int, default=1)
-    parser.add_argument("--special_instruction", type=str, default=None, choices=["ag", "stacking"])
+    parser.add_argument("--special_instruction", type=str, default=None, choices=["ag", "stacking", "text", "image"])
     parser.set_defaults(reflection=True)