diff --git a/README.md b/README.md
index a22bed14..3cc06adf 100644
--- a/README.md
+++ b/README.md
@@ -111,9 +111,9 @@ To evaluate the performance of uncertainty estimation methods consider a quick e
 ```
 HYDRA_CONFIG=../examples/configs/polygraph_eval_coqa.yaml python ./scripts/polygraph_eval \
     dataset="coqa" \
-    model="databricks/dolly-v2-3b" \
+    model.path="databricks/dolly-v2-3b" \
     save_path="./workdir/output" \
-    seed=[1,2,3,4,5]
+    "seed=[1,2,3,4,5]"
 ```
 
 Use [`visualization_tables.ipynb`](https://github.com/IINemo/lm-polygraph/blob/main/notebooks/vizualization_tables.ipynb) or [`result_tables.ipynb`](https://github.com/IINemo/lm-polygraph/blob/main/notebooks/result_tables.ipynb) to generate the summarizing tables for an experiment.
diff --git a/scripts/polygraph_eval b/scripts/polygraph_eval
index 20cd8e92..cae0b9f8 100755
--- a/scripts/polygraph_eval
+++ b/scripts/polygraph_eval
@@ -352,6 +352,7 @@ def get_generation_metrics(args):
             RougeMetric("rouge1"),
             RougeMetric("rouge2"),
             RougeMetric("rougeL"),
+            BLEUMetric(),
             BertScoreMetric('rh'),
             SbertMetric(),
             AccuracyMetric(
@@ -360,8 +361,9 @@ def get_generation_metrics(args):
                 normalize = getattr(args, "normalize", False),
             ),
             AlignScore(),
-            OpenAIFactCheck(cache_path=args.cache_path),
         ]
+        if getattr(args, "use_claim_ue", False):
+            result += [OpenAIFactCheck(cache_path=args.cache_path)]
         if args.task == "nmt":
             ignore_regex = getattr(args, "source_ignore_regex", None)
             result += [Comet(source_ignore_regex = ignore_regex)]
diff --git a/src/lm_polygraph/generation_metrics/__init__.py b/src/lm_polygraph/generation_metrics/__init__.py
index c0fabd72..89cd8765 100644
--- a/src/lm_polygraph/generation_metrics/__init__.py
+++ b/src/lm_polygraph/generation_metrics/__init__.py
@@ -1,4 +1,5 @@
 from .rouge import RougeMetric
+from .bleu import BLEUMetric
 from .model_score import ModelScoreSeqMetric, ModelScoreTokenwiseMetric
 from .bart_score import BartScoreSeqMetric
 from .accuracy import AccuracyMetric
diff --git a/src/lm_polygraph/generation_metrics/bleu.py b/src/lm_polygraph/generation_metrics/bleu.py
new file mode 100644
index 00000000..865423ae
--- /dev/null
+++ b/src/lm_polygraph/generation_metrics/bleu.py
@@ -0,0 +1,47 @@
+import numpy as np
+from sacrebleu.metrics import BLEU
+
+from typing import List, Dict
+from .generation_metric import GenerationMetric
+
+
+class BLEUMetric(GenerationMetric):
+    """
+    Calculates BLEU metric between model-generated texts and ground truth texts.
+    """
+
+    def __init__(self):
+        super().__init__(["greedy_texts"], "sequence")
+        self.scorer = BLEU(effective_order=True, lowercase=True)
+
+    def __str__(self):
+        return "BLEU"
+
+    def _score_single(self, t1: str, t2: str):
+        return self.scorer.sentence_score(
+            t1.strip().rstrip("."), [t2.strip().rstrip(".")]
+        ).score
+
+    def __call__(
+        self,
+        stats: Dict[str, np.ndarray],
+        target_texts: List[str],
+        target_tokens: List[List[int]],
+    ) -> np.ndarray:
+        """
+        Calculates BLEU score between stats['greedy_texts'] and target_texts.
+
+        Parameters:
+            stats (Dict[str, np.ndarray]): input statistics, which for multiple samples includes:
+                * model-generated texts in 'greedy_texts'
+            target_texts (List[str]): ground-truth texts
+            target_tokens (List[List[int]]): corresponding token splits for each target text
+        Returns:
+            np.ndarray: list of BLEU Scores for each sample in input.
+        """
+        return np.array(
+            [
+                self._score_single(hyp, ref)
+                for hyp, ref in zip(stats["greedy_texts"], target_texts)
+            ]
+        )