diff --git a/haystack/nodes/prompt/invocation_layer/hugging_face.py b/haystack/nodes/prompt/invocation_layer/hugging_face.py
index 277d8d4a4..a0a48d902 100644
--- a/haystack/nodes/prompt/invocation_layer/hugging_face.py
+++ b/haystack/nodes/prompt/invocation_layer/hugging_face.py
@@ -195,6 +195,7 @@ class HFLocalInvocationLayer(PromptModelInvocationLayer):
         """
         output: List[Dict[str, str]] = []
         stop_words = kwargs.pop("stop_words", None)
+        top_k = kwargs.pop("top_k", None)
         # either stream is True (will use default handler) or stream_handler is provided for custom handler
         stream = kwargs.get("stream", self.stream)
         stream_handler = kwargs.get("stream_handler", self.stream_handler)
@@ -238,21 +239,12 @@ class HFLocalInvocationLayer(PromptModelInvocationLayer):
             if stop_words:
                 sw = StopWordsCriteria(tokenizer=self.pipe.tokenizer, stop_words=stop_words, device=self.pipe.device)
                 model_input_kwargs["stopping_criteria"] = StoppingCriteriaList([sw])
-
-            if "num_beams" in model_input_kwargs:
-                num_beams = model_input_kwargs["num_beams"]
-                if (
-                    "num_return_sequences" in model_input_kwargs
-                    and model_input_kwargs["num_return_sequences"] > num_beams
-                ):
-                    num_return_sequences = model_input_kwargs["num_return_sequences"]
-                    logger.warning(
-                        "num_return_sequences %s should not be larger than num_beams %s, hence setting it equal to num_beams",
-                        num_return_sequences,
-                        num_beams,
-                    )
-                    model_input_kwargs["num_return_sequences"] = num_beams
-
+            if top_k:
+                model_input_kwargs["num_return_sequences"] = top_k
+                if "num_beams" not in model_input_kwargs or model_input_kwargs["num_beams"] < top_k:
+                    if "num_beams" in model_input_kwargs:
+                        logger.warning("num_beams should not be less than top_k, hence setting it to %s", top_k)
+                    model_input_kwargs["num_beams"] = top_k
             # max_new_tokens is used for text-generation and max_length for text2text-generation
             if is_text_generation:
                 model_input_kwargs["max_new_tokens"] = model_input_kwargs.pop("max_length", self.max_length)
diff --git a/test/prompt/invocation_layer/test_hugging_face.py b/test/prompt/invocation_layer/test_hugging_face.py
index 2ff1f52e1..e5ca043ef 100644
--- a/test/prompt/invocation_layer/test_hugging_face.py
+++ b/test/prompt/invocation_layer/test_hugging_face.py
@@ -205,48 +205,6 @@ def test_ensure_token_limit_negative(caplog):
     assert caplog.records[0].message == expected_message
 
 
-@pytest.mark.unit
-def test_num_return_sequences_no_larger_than_num_beams(mock_pipeline, mock_get_task, caplog):
-    """
-    Test that num_return_sequences cannot be larger than num_beams and that a warning is logged
-    """
-
-    layer = HFLocalInvocationLayer("google/flan-t5-base")
-
-    with patch.object(layer.pipe, "run_single", MagicMock()):
-        layer.invoke(prompt="What does 42 mean?", generation_kwargs={"num_beams": 5, "num_return_sequences": 8})
-
-    expected_message = (
-        "num_return_sequences 8 should not be larger than num_beams 5, hence setting it equal to num_beams"
-    )
-    # check that the warning is logged
-    assert caplog.records[0].message == expected_message
-
-    # check that num_return_sequences is set to num_beams
-    _, kwargs = layer.pipe.call_args
-    assert kwargs["num_beams"] == 5
-    assert kwargs["num_return_sequences"] == 5
-
-
-@pytest.mark.unit
-def test_num_beams_larger_than_num_return_sequences(mock_pipeline, mock_get_task, caplog):
-    """
-    Test that num_beams can be larger than num_return_sequences and that no warning is logged
-    """
-    layer = HFLocalInvocationLayer("google/flan-t5-base")
-
-    with patch.object(layer.pipe, "run_single", MagicMock()):
-        layer.invoke(prompt="What does 42 mean?", generation_kwargs={"num_beams": 8, "num_return_sequences": 5})
-
-    # check that no warning is logged
-    assert not caplog.records
-
-    # check that num_return_sequences remains unchanged
-    _, kwargs = layer.pipe.call_args
-    assert kwargs["num_beams"] == 8
-    assert kwargs["num_return_sequences"] == 5
-
-
 @pytest.mark.unit
 def test_constructor_with_custom_pretrained_model(mock_pipeline, mock_get_task):
     """