diff --git a/README.md b/README.md
index 63832b9..5f8527b 100644
--- a/README.md
+++ b/README.md
@@ -111,7 +111,7 @@ Save the edited file and `source` it
 source set_env.sh
 ```
   
-Run the below line to unset the environment variables  
+Run the below line to unset the environment variables (when done with experiments)
 ```shell  
 sh unset_env.sh
 ```  
@@ -139,7 +139,7 @@ Pass the data split name to `-d` flag. Possible values are: `5-dpd`, `10-dpd`, `
 Example training command: `sh train_baseline.sh -d 50-dpd`  
   
 ### Belief State Prediction  
-Choose a checkpoint of the saved baseline model to generate belief state predictions.  
+Choose a checkpoint of the saved baseline model to generate belief states.  
   
 Set the `MODEL_CHECKPOINT` environment variable with the path to the chosen model checkpoint. It should only contain the path from the "experiment-{datetime}" folder.  
 ```shell  
@@ -172,7 +172,7 @@ python evaluate.py
 | 50-dpd | 28.64 |  
 | 100-dpd | 33.11 |  
 | 125-dpd | 35.79 |  
-| 250-dpd | 40.38 |
+| 250-dpd | **40.38** |
 
 ## Prompt Learning Experiments
 
@@ -181,8 +181,9 @@ The data for training the prompt learning model is available under [data/prompt-
 `create_dataset.py` ([link](utils/create_dataset.py)) has the scripts for converting/creating the data for training the prompt-based model.
 
 > **Note:**
-> Running `create_dataset.py` can take some time as it needs to download, install and run Stanford CoreNLP `stanza` package.
-> All the data required for training the prompt-based model is available under [data](data) directory of this repo.
+> Running `create_dataset.py` can take some time as it needs to download, install and run Stanford CoreNLP `stanza` package. This scripts downloads coreNLP files of size `~1GB` and requires significant amount of RAM and processor capabilities to run efficiently.
+>
+> All the data required for training the prompt-based model is already available under the [data](data) directory of this repo.
 
 ### Install the requirements  
 After following the environment setup steps in the previous [section](#environment-setup), install the required python modules for prompt model training.  
@@ -206,17 +207,17 @@ Example training command: `sh train_baseline.sh -d 50-dpd`
 
 **Some `train_prompting.sh` flags**:
 
-`--num_epochs 10` - Number of epochs
+`--num_epochs` - Number of epochs
 
-`--learning_rate 5e-5` - Initial learning rate for Optimizer
+`--learning_rate` - Initial learning rate for Optimizer
 
-`--with_inverse_prompt` - Use Inverse Prompt while training
+`--with_inverse_prompt` - Use Inverse Prompt while training **(recommended)**
 
-`--inverse_prompt_weight 0.1` - Weight of the inverse prompt for loss function
+`--inverse_prompt_weight` - Weight of the inverse prompt for loss function
 
 **Note:** The defaults in `train_prompting.sh` are the best performing values.
 
-### Belief State Generations (Prompt Generation)
+### Belief State Generations (Prompt-based slot generation)
 Now, the belief states can be generated by prompting. Choose a prompt fine-tuned model from the saved epochs and run the below script to generate belief states. 
 
 Generate belief states by running the below script:
@@ -228,9 +229,9 @@ The argument `-m` takes the relative path of saved model from `SAVED_MODELS_PROM
 
 Example: `sh test_prompting.sh -m 50-dpd/experiment-20221003T172424/epoch-09`
 
-The generated belief states (outputs) are saved under `OUTPUTS_DIR_PROMPT` folder. Some of the best outputs are uploaded to this repository and can be found under [outputs](outputs) folder. 
+The generated belief states (outputs) are saved under `OUTPUTS_DIR_PROMPT` folder. Some of the output files are uploaded to this repository and can be found under [outputs](outputs/prompt-learning) folder. 
 
-### Prompting Evaluation  
+### Evaluation of prompt-based generations
 The standard Joint Goal Accuracy (**JGA**) is used to evaluate the belief state predictions. In order to exclude the influence of wrongly extracted values, **JGA*** is computed only for values that are extracted correctly at each turn.
   
 The [evaluate.py](prompt-learning/evaluate.py) file can be used to verify the below JGA scores.
@@ -245,7 +246,7 @@ python evaluate.py -o path/to/outputs/file
 | 5-dpd | 30.66 | 71.04 |  
 | 10-dpd | 42.65 | 86.43 |  
 | 50-dpd | 47.06 | 91.63 |  
-| 100-dpd | 47.74 | 92.31 |  
+| 100-dpd | **47.74** | **92.31** |  
 | 125-dpd | 46.49 | 91.86 |  
 | 250-dpd | 47.06 | 92.08 |