Update README.md

README.md

@@ -61,6 +61,7 @@ To this end, we introduce two new corpora: one comprises PNAS abstract-significa
 We finetune the scientifc abstract simplification task using an encoder-decoder Transformer model (a variant of Flan-T5).
 Our model is first tuned with multiple discrete instructions by mixing four relevant tasks in a challenge-proportional manner.
 Then we continue tuning the model solely with the abstract-significance corpus.
+The model can generate better lay summaries compared with models finetuned only with the abstract-significance corpus and models finetuned with task mixtures in traditonal ways.
 We hope our work can pave the last mile of scientific understanding and let people better enjoy the fruits of open science.
 
 
@@ -75,7 +76,6 @@ We hope our work can pave the last mile of scientific understanding and let peop
 - **Parent Model:** [FLAN-T5-large](https://huggingface.co/google/flan-t5-large)
 
 
-
 # Usage
 
 Use the code below to get started with the model. Remember to prepend the `INSTRUCTION` for best performance.
@@ -109,7 +109,7 @@ print(tokenizer.decode(decoded_ids[0], skip_special_tokens=True))
 |----------------------------------|-----------------------------|------------------------------------|--------------------------------------|--------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|
 | Scientific Abstract-Significance | 3,030/200/200               | 707,071, 375,433                   | 45,697, 24,901                       | 46,985, 24,426                 | -                                                                                                                                      |
 | Editor Abstract                  | 732/91/92                   | 154,808, 194,721                   | 19,675, 24,421                       | 19,539, 24,332                 | -                                                                                                                                      |
-| Wiki Auto                        | 28,364/1,000/1,000          | 18,239,990, 12,547,272             | 643,157, 444,034                     | 642549, 444883                 | We used the ACL version, adopted from Huggingface datasets. The validation and test samples are split from the corpus and kept frozen. |
+| Wiki Auto                        | 28,364/1,000/1,000          | 18,239,990, 12,547,272             | 643,157, 444,034                     | 642549, 444,883                | We used the ACL version, adopted from Huggingface datasets. The validation and test samples are split from the corpus and kept frozen. |
 | CNN/DailyMail                    | 287,113/13,368/11,490       | -                                  | -                                    | -                              | We used the 2.0 version, adopted from Huggingface datasets.                                                                            |
 
 
@@ -130,7 +130,7 @@ We finetuned the base model (flan-t5-large) on multiple relevant tasks with stan
 
 - Retuning: In this stage, we continued finetuning the checkpoint solely with the Scientific Abstract-Significance corpus till optimal validation loss was observed.
 
-The multi-instruction tuning and the retuning took roughly 63 hours and 8 hours, respectively, on two NVIDIA RTX A5000 (24GB memory each) GPUs. We saved the checkpoint with the lowest validation loss for inference. We used the AdamW optimizer and a learning rate of 3e-5 with fully sharded data parallel strategy across training stages. 
+The multi-instruction tuning and the retuning took roughly 63 hours and 8 hours, respectively, on two NVIDIA RTX A5000 (24GB memory each) GPUs. We saved the checkpoint with the lowest validation loss for inference. We used the AdamW optimizer and a learning rate of 3e-5 with fully sharded data parallel strategy across training stages. The batch size equals to 1.
 
  
 # Evaluation