diff --git a/llava/mm_utils.py b/llava/mm_utils.py
index 2662b91db..23fdac956 100644
--- a/llava/mm_utils.py
+++ b/llava/mm_utils.py
@@ -77,23 +77,26 @@ class KeywordsStoppingCriteria(StoppingCriteria):
     def __init__(self, keywords, tokenizer, input_ids):
         self.keywords = keywords
         self.keyword_ids = []
+        self.max_keyword_len = 0
         for keyword in keywords:
             cur_keyword_ids = tokenizer(keyword).input_ids
             if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
                 cur_keyword_ids = cur_keyword_ids[1:]
+            if len(cur_keyword_ids) > self.max_keyword_len:
+                self.max_keyword_len = len(cur_keyword_ids)
             self.keyword_ids.append(torch.tensor(cur_keyword_ids))
         self.tokenizer = tokenizer
         self.start_len = input_ids.shape[1]
 
     def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
         assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"  # TODO
-        offset = min(output_ids.shape[1] - self.start_len, 3)
+        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
         self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
         for keyword_id in self.keyword_ids:
-            if output_ids[0, -keyword_id.shape[0]:] == keyword_id:
+            if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
                 return True
         outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
         for keyword in self.keywords:
             if keyword in outputs:
                 return True
-        return False
+        return False
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