diff --git a/src/promptflow-evals/CHANGELOG.md b/src/promptflow-evals/CHANGELOG.md
index 3be508e2bd6..997080a7e10 100644
--- a/src/promptflow-evals/CHANGELOG.md
+++ b/src/promptflow-evals/CHANGELOG.md
@@ -8,6 +8,7 @@
 
 ### Bugs Fixed
 - Large simulation was causing a jinja exception, this has been fixed.
+- Fixed the issue where the relative data path was not working with the evaluate API when using multiple evaluators.
 
 ### Improvements
 - Converted built-in evaluators to async-based implementation, leveraging async batch run for performance improvement. Introduced `PF_EVALS_BATCH_USE_ASYNC` environment variable to enable/disable async batch run, with the default set to False.
diff --git a/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py b/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
index 8f2b979afb9..f6f40c73376 100644
--- a/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
+++ b/src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py
@@ -2,6 +2,7 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # ---------------------------------------------------------
 import inspect
+import os
 import re
 from typing import Any, Callable, Dict, Optional, Set, Tuple
 
@@ -442,7 +443,15 @@ def _evaluate(  # pylint: disable=too-many-locals
     # Batch Run
     evaluators_info = {}
     use_pf_client = kwargs.get("_use_pf_client", True)
-    batch_run_client = ProxyClient(pf_client) if use_pf_client else CodeClient()
+    if use_pf_client:
+        batch_run_client = ProxyClient(pf_client)
+
+        # Ensure the absolute path is passed to pf.run, as relative path doesn't work with
+        # multiple evaluators. If the path is already absolute, abspath will return the original path.
+        data = os.path.abspath(data)
+    else:
+        batch_run_client = CodeClient()
+        data = input_data_df
 
     with BatchRunContext(batch_run_client):
         for evaluator_name, evaluator in evaluators.items():
@@ -452,7 +461,7 @@ def _evaluate(  # pylint: disable=too-many-locals
                 run=target_run,
                 evaluator_name=evaluator_name,
                 column_mapping=evaluator_config.get(evaluator_name, evaluator_config.get("default", None)),
-                data=input_data_df if isinstance(batch_run_client, CodeClient) else data,
+                data=data,
                 stream=True,
                 name=kwargs.get("_run_name"),
             )
diff --git a/src/promptflow-evals/tests/evals/e2etests/test_evaluate.py b/src/promptflow-evals/tests/evals/e2etests/test_evaluate.py
index 38dde389654..8e9b6f1fddb 100644
--- a/src/promptflow-evals/tests/evals/e2etests/test_evaluate.py
+++ b/src/promptflow-evals/tests/evals/e2etests/test_evaluate.py
@@ -125,6 +125,41 @@ def test_evaluate_with_groundedness_evaluator(self, model_config, data_file):
         assert row_result_df["outputs.f1_score.f1_score"][2] == 1
         assert result["studio_url"] is None
 
+    def test_evaluate_with_relative_data_path(self, model_config):
+        original_working_dir = os.getcwd()
+
+        try:
+            working_dir = os.path.dirname(__file__)
+            os.chdir(working_dir)
+
+            data_file = "data/evaluate_test_data.jsonl"
+            input_data = pd.read_json(data_file, lines=True)
+
+            groundedness_eval = GroundednessEvaluator(model_config)
+            fluency_eval = FluencyEvaluator(model_config)
+
+            # Run the evaluation
+            result = evaluate(
+                data=data_file,
+                evaluators={"grounded": groundedness_eval, "fluency": fluency_eval},
+            )
+
+            row_result_df = pd.DataFrame(result["rows"])
+            metrics = result["metrics"]
+
+            # Validate the results
+            assert result is not None
+            assert result["rows"] is not None
+            assert row_result_df.shape[0] == len(input_data)
+
+            assert "outputs.grounded.gpt_groundedness" in row_result_df.columns.to_list()
+            assert "outputs.fluency.gpt_fluency" in row_result_df.columns.to_list()
+
+            assert "grounded.gpt_groundedness" in metrics.keys()
+            assert "fluency.gpt_fluency" in metrics.keys()
+        finally:
+            os.chdir(original_working_dir)
+
     @pytest.mark.azuretest
     def test_evaluate_with_content_safety_evaluator(self, project_scope, data_file):
         input_data = pd.read_json(data_file, lines=True)