diff --git a/dan/ocr/predict/inference.py b/dan/ocr/predict/inference.py
index 218117b098da5e62d27d952808678ccaa68a2beb..d9f30cf625131f5e4da3ae436ecdc24df6f01f39 100644
--- a/dan/ocr/predict/inference.py
+++ b/dan/ocr/predict/inference.py
@@ -92,7 +92,7 @@ class DAN:
         self.decoder = decoder
         self.lm_decoder = None
 
-        if use_language_model:
+        if use_language_model and parameters["lm_decoder"]["language_model_weight"] > 0:
             self.lm_decoder = CTCLanguageDecoder(
                 language_model_path=parameters["lm_decoder"]["language_model_path"],
                 lexicon_path=parameters["lm_decoder"]["lexicon_path"],
@@ -478,6 +478,9 @@ def run(
     )
     batch_size = 1 if use_language_model else batch_size
 
+    # Do not use LM with invalid LM weight
+    use_language_model = dan_model.lm_decoder is not None
+
     images = image_dir.rglob(f"*{image_extension}") if not image else [image]
     for image_batch in list_to_batches(images, n=batch_size):
         process_batch(
diff --git a/tests/data/prediction/parameters.yml b/tests/data/prediction/parameters.yml
index 88a43d9c171ee483e734f01b0d99250f10c5d5ec..9f41f5cfc056a2211c21df4fdd47a2697afe32cd 100644
--- a/tests/data/prediction/parameters.yml
+++ b/tests/data/prediction/parameters.yml
@@ -22,8 +22,8 @@ parameters:
     - type: "max_resize"
       max_height: 1500
       max_width: 1500
-  language_model:
-    model: tests/data/prediction/language_model.arpa
-    lexicon: tests/data/prediction/language_lexicon.txt
-    tokens: tests/data/prediction/language_tokens.txt
-    weight: 1.0
+  lm_decoder:
+    language_model_path: tests/data/prediction/language_model.arpa
+    lexicon_path: tests/data/prediction/language_lexicon.txt
+    tokens_path: tests/data/prediction/language_tokens.txt
+    language_model_weight: 1.0
diff --git a/tests/test_prediction.py b/tests/test_prediction.py
index 30576b1b5136e7459dc5843bbaab172a9173162f..2474473ec683005ab261bd30fd25ab09733ce972 100644
--- a/tests/test_prediction.py
+++ b/tests/test_prediction.py
@@ -545,19 +545,28 @@ def test_run_prediction_batch(
                 {
                     "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241",
                     "language_model": {
+<<<<<<< HEAD
                         "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241",
                         "confidence": 0.92,
+=======
+                        "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241"
+>>>>>>> c80c413 (Write tests for LM decoding)
                     },
                 },
                 {
                     "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376",
                     "language_model": {
+<<<<<<< HEAD
                         "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376",
                         "confidence": 0.88,
+=======
+                        "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376"
+>>>>>>> c80c413 (Write tests for LM decoding)
                     },
                 },
                 {
                     "text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ14 31",
+<<<<<<< HEAD
                     "language_model": {
                         "text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ14 31",
                         "confidence": 0.86,
@@ -569,6 +578,13 @@ def test_run_prediction_batch(
                         "text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère",
                         "confidence": 0.89,
                     },
+=======
+                    "language_model": {"text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ1431"},
+                },
+                {
+                    "text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère",
+                    "language_model": {"text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère"},
+>>>>>>> c80c413 (Write tests for LM decoding)
                 },
             ],
         ),
@@ -584,19 +600,28 @@ def test_run_prediction_batch(
                 {
                     "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241",
                     "language_model": {
+<<<<<<< HEAD
                         "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241",
                         "confidence": 0.90,
+=======
+                        "text": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier ⓅPatron?12241"
+>>>>>>> c80c413 (Write tests for LM decoding)
                     },
                 },
                 {
                     "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376",
                     "language_model": {
+<<<<<<< HEAD
                         "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376",
                         "confidence": 0.84,
+=======
+                        "text": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁS Ⓚch ⓄE dactylo Ⓟ18376"
+>>>>>>> c80c413 (Write tests for LM decoding)
                     },
                 },
                 {
                     "text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ14 31",
+<<<<<<< HEAD
                     "language_model": {
                         "text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ14331",
                         "confidence": 0.83,
@@ -608,6 +633,13 @@ def test_run_prediction_batch(
                         "text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère",
                         "confidence": 0.86,
                     },
+=======
+                    "language_model": {"text": "Ⓢd ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF Ⓞd Ⓟ14331"},
+                },
+                {
+                    "text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère",
+                    "language_model": {"text": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS Ⓒv ⓀBelle mère"},
+>>>>>>> c80c413 (Write tests for LM decoding)
                 },
             ],
         ),
@@ -628,10 +660,18 @@ def test_run_prediction_batch(
         ),
     ),
 )
+<<<<<<< HEAD
+=======
+@pytest.mark.parametrize("batch_size", [1, 2])
+>>>>>>> c80c413 (Write tests for LM decoding)
 def test_run_prediction_language_model(
     image_names,
     language_model_weight,
     expected_predictions,
+<<<<<<< HEAD
+=======
+    batch_size,
+>>>>>>> c80c413 (Write tests for LM decoding)
     tmp_path,
 ):
     # Make tmpdir and copy needed images inside
@@ -645,7 +685,11 @@ def test_run_prediction_language_model(
 
     # Update language_model_weight in parameters.yml
     params = read_yaml(PREDICTION_DATA_PATH / "parameters.yml")
+<<<<<<< HEAD
     params["parameters"]["language_model"]["weight"] = language_model_weight
+=======
+    params["parameters"]["lm_decoder"]["language_model_weight"] = language_model_weight
+>>>>>>> c80c413 (Write tests for LM decoding)
     yaml.dump(params, (tmp_path / "parameters.yml").open("w"))
 
     run_prediction(
@@ -669,7 +713,11 @@ def test_run_prediction_language_model(
         max_object_height=None,
         image_extension=".png",
         gpu_device=None,
+<<<<<<< HEAD
         batch_size=1,
+=======
+        batch_size=batch_size,
+>>>>>>> c80c413 (Write tests for LM decoding)
         tokens=parse_tokens(PREDICTION_DATA_PATH / "tokens.yml"),
         start_token=None,
         use_language_model=True,
@@ -686,7 +734,10 @@ def test_run_prediction_language_model(
                 prediction["language_model"]["text"]
                 == expected_prediction["language_model"]["text"]
             )
+<<<<<<< HEAD
             assert np.isclose(
                 prediction["language_model"]["confidence"],
                 expected_prediction["language_model"]["confidence"],
             )
+=======
+>>>>>>> c80c413 (Write tests for LM decoding)