diff --git a/benchmark.py b/benchmark.py
index dfce857..9a1236f 100644
--- a/benchmark.py
+++ b/benchmark.py
@@ -194,6 +194,36 @@ def eval_align_nlg(ckpt_path, comment='', base_model='roberta-large', batch_size
     evaluator.evaluate()
     timer.finish(name)
 
+def eval_gptscore(api_key, gpt_model='davinci003', tasks=ALL_TASKS):
+        gptscore = GPTScoreScorer(api_key=api_key, gpt_model=gpt_model)
+        evaluator = Evaluator(eval_tasks=tasks, align_func=gptscore.scorer, is_save_all_tables=IS_SAVE_ALL_TABLES)
+        evaluator.result_save_name = f"nlg_eval_fact/baselines/GPTScore-{gpt_model}"
+        evaluator.evaluate()
+    
+def eval_chatgptluo2023(api_key, chat_model='gpt-3.5-turbo', tasks=['qags_cnndm']):
+    chatgpt = ChatGPTLuo2023Scorer(task=tasks, api_key=api_key, chat_model=chat_model)
+    evaluator = Evaluator(eval_tasks=tasks, align_func=chatgpt.scorer, is_save_all_tables=IS_SAVE_ALL_TABLES)
+    evaluator.result_save_name = f"nlg_eval_fact/baselines/ChatGPTLuo2023-{chat_model}"
+    evaluator.evaluate()
+
+def eval_chatgptgao2023(api_key, chat_model='gpt-3.5-turbo', tasks=['qags_cnndm']):
+    chatgpt = ChatGPTGao2023Scorer(task=tasks, api_key=api_key, chat_model=chat_model)
+    evaluator = Evaluator(eval_tasks=tasks, align_func=chatgpt.scorer, is_save_all_tables=IS_SAVE_ALL_TABLES)
+    evaluator.result_save_name = f"nlg_eval_fact/baselines/ChatGPTGao2023-{chat_model}"
+    evaluator.evaluate()
+
+def eval_chatgptyichen2023(api_key, chat_model='gpt-3.5-turbo', tasks=['qags_cnndm']):
+    chatgpt = ChatGPTYiChen2023Scorer(task=tasks, api_key=api_key, chat_model=chat_model)
+    evaluator = Evaluator(eval_tasks=tasks, align_func=chatgpt.scorer, is_save_all_tables=IS_SAVE_ALL_TABLES)
+    evaluator.result_save_name = f"nlg_eval_fact/baselines/ChatGPTYiChen2023-{chat_model}"
+    evaluator.evaluate()
+
+def eval_chatgptshiqichen2023(api_key, chat_model='gpt-3.5-turbo', tasks=['qags_cnndm']):
+    chatgpt = ChatGPTShiqiChen2023Scorer(task=tasks, api_key=api_key, chat_model=chat_model)
+    evaluator = Evaluator(eval_tasks=tasks, align_func=chatgpt.scorer, is_save_all_tables=IS_SAVE_ALL_TABLES)
+    evaluator.result_save_name = f"nlg_eval_fact/baselines/ChatGPTShiqiChen2023-{chat_model}"
+        evaluator.evaluate()
+
 def run_benchmarks(args, argugment_error):
     os.makedirs('exp_results/baselines', exist_ok=True)
     os.makedirs('exp_results/align_eval', exist_ok=True)