Merge pull request #3 from cooelf/main

update readme & argument helps

README.md

@@ -1,4 +1,4 @@
-# Auto-CoT: Automatic Chain of Thought Prompting in Large Language Models
+# Auto-CoT: Automatic Chain of Thought Prompting in Large Language Models (ICLR 2023)
 
 [![Open Auto-CoT in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/amazon-science/auto-cot/blob/main/try_cot_colab.ipynb)
 
@@ -56,11 +56,11 @@ python run_inference.py \
 
 ## Citing Auto-CoT
 ```
-@article{zhang2022automatic,
+@inproceedings{zhang2023automatic,
   title={Automatic Chain of Thought Prompting in Large Language Models},
   author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Smola, Alex},
-  journal={arXiv preprint arXiv:2210.03493},
-  year={2022}
+  booktitle={The Eleventh International Conference on Learning Representations (ICLR 2023)},
+  year={2023}
 }
 ```
 

run_demo.py

@@ -27,7 +27,7 @@ def parse_arguments():
     )
     parser.add_argument("--random_seed", type=int, default=192, help="random seed")
     parser.add_argument(
-        "--encoder", type=str, default="all-MiniLM-L6-v2", help="where to save the contructed demonstrations"
+        "--encoder", type=str, default="all-MiniLM-L6-v2", help="which sentence-transformer encoder for clustering"
     )
     parser.add_argument(
         "--sampling", type=str, default="center", help="whether to sample the cluster center first"

run_inference.py

@@ -118,10 +118,10 @@ def parse_arguments():
         "--dataset", type=str, default="multiarith", choices=["aqua", "gsm8k", "commonsensqa", "addsub", "multiarith",  "strategyqa", "svamp", "singleeq", "coin_flip", "last_letters"], help="dataset used for experiment"
     )
     parser.add_argument(
-        "--demo_path", type=str, default="demos/multiarith", help="dataset used for experiment"
+        "--demo_path", type=str, default="demos/multiarith", help="pre-generated demos used for experiment"
     )
     parser.add_argument(
-        "--resume_id", type=int, default=0, help="whether to limit test dataset size. if 0, the dataset size is unlimited and we use all the samples in the dataset for testing."
+        "--resume_id", type=int, default=0, help="resume from which question id (current line number in the output file), if the experiment fails accidently (e.g., network error)"
     )
     parser.add_argument("--minibatch_size", type=int, default=1, choices=[1], help="minibatch size should be 1 because GPT-3 API takes only 1 input for each request")
 
@@ -147,10 +147,10 @@ def parse_arguments():
         "--limit_dataset_size", type=int, default=0, help="whether to limit test dataset size. if 0, the dataset size is unlimited and we use all the samples in the dataset for testing."
     )
     parser.add_argument(
-        "--api_time_interval", type=float, default=1.0, help=""
+        "--api_time_interval", type=float, default=1.0, help="sleep between runs to avoid excedding the rate limit of openai api"
     )
     parser.add_argument(
-        "--temperature", type=float, default=0, help=""
+        "--temperature", type=float, default=0, help="temperature for GPT-3"
     )
     parser.add_argument(
         "--log_dir", type=str, default="./log/", help="log directory"