diff --git a/docs/tutorials/adding_asset.md b/docs/tutorials/adding_asset.md
index aa10db8f..65e0e044 100644
--- a/docs/tutorials/adding_asset.md
+++ b/docs/tutorials/adding_asset.md
@@ -27,6 +27,13 @@ def config():
 	# 		}
 	# 	}
 	# }
+	#
+	# If the Dataset you are using does not have your required splits by default,
+	# "custom_test_split" and "custom_train_split" can be used instead. These are
+	# usually strings, but their structure is dictated by the data loader in a
+	# specific dataset. The framework supports absolute paths, relative paths (
+	# relative to `data_dir/*Dataset/`) or special paths prefixed with `:data_dir:`,
+	# which are resolved relative to `data_dir`.
 
 def prompt(input_sample):
 	# This function receives an input_sample and pre-processes it into the
diff --git a/llmebench/datasets/TyDiQA.py b/llmebench/datasets/TyDiQA.py
index cec6edce..5c07c0f9 100644
--- a/llmebench/datasets/TyDiQA.py
+++ b/llmebench/datasets/TyDiQA.py
@@ -22,7 +22,7 @@ def metadata():
             "license": "Apache License Version 2.0",
             "splits": {
                 "dev": "tydiqa-goldp-dev-arabic.json",
-                "train": ":depends:ARCD/arcd-train.json",
+                "train": ":data_dir:ARCD/arcd-train.json",
             },
             "task_type": TaskType.QuestionAnswering,
         }
diff --git a/llmebench/datasets/UnifiedFCFactuality.py b/llmebench/datasets/UnifiedFCFactuality.py
index d106d982..a6e5ff2a 100644
--- a/llmebench/datasets/UnifiedFCFactuality.py
+++ b/llmebench/datasets/UnifiedFCFactuality.py
@@ -25,7 +25,7 @@ def metadata():
             "license": "Research Purpose Only",
             "splits": {
                 "test": "ramy_arabic_fact_checking.tsv",
-                "train": ":depends:ANSStance/claim/train.csv",
+                "train": ":data_dir:ANSStance/claim/train.csv",
             },
             "task_type": TaskType.Classification,
             "class_labels": ["true", "false"],
diff --git a/llmebench/datasets/UnifiedFCStance.py b/llmebench/datasets/UnifiedFCStance.py
index 288599f6..915e1215 100644
--- a/llmebench/datasets/UnifiedFCStance.py
+++ b/llmebench/datasets/UnifiedFCStance.py
@@ -27,7 +27,7 @@ def metadata():
             "license": "Research Purpose Only",
             "splits": {
                 "test": "ramy_arabic_stance.jsonl",
-                "train": ":depends:ANSStance/stance/train.csv",
+                "train": ":data_dir:ANSStance/stance/train.csv",
             },
             "task_type": TaskType.Classification,
             "class_labels": ["agree", "disagree", "discuss", "unrelated"],
diff --git a/llmebench/datasets/XQuAD.py b/llmebench/datasets/XQuAD.py
index fc5ae0d0..9f5e6d64 100644
--- a/llmebench/datasets/XQuAD.py
+++ b/llmebench/datasets/XQuAD.py
@@ -23,7 +23,7 @@ def metadata():
             "license": "CC-BY-SA4.0",
             "splits": {
                 "test": "xquad.ar.json",
-                "train": ":depends:ARCD/arcd-train.json",
+                "train": ":data_dir:ARCD/arcd-train.json",
             },
             "task_type": TaskType.QuestionAnswering,
         }
diff --git a/llmebench/utils.py b/llmebench/utils.py
index beeca1f3..65fbe7b9 100644
--- a/llmebench/utils.py
+++ b/llmebench/utils.py
@@ -51,6 +51,7 @@ def get_data_paths(config, split):
     assert split in ["train", "test"]
 
     dataset_args = config.get("dataset_args", {})
+    dataset_args["data_dir"] = ""
     dataset = config["dataset"](**dataset_args)
 
     if split == "test":
@@ -129,10 +130,10 @@ def resolve_path(path, dataset, data_dir):
     if not isinstance(data_dir, Path):
         data_dir = Path(data_dir)
 
-    if not str(path).startswith(":depends:") and path.is_absolute():
+    if not str(path).startswith(":data_dir:") and path.is_absolute():
         return path
-    elif str(path).startswith(":depends:"):
-        return data_dir / str(path)[len(":depends:") :]
+    elif str(path).startswith(":data_dir:"):
+        return data_dir / str(path)[len(":data_dir:") :]
     else:
         dataset_name = dataset.__class__.__name__
         if dataset_name.endswith("Dataset"):