Update 'README.md'

README.md

@@ -11,7 +11,19 @@ We also present:
 
 Our results demonstrate that LLM-based G2P systems can outperform traditional tools, especially in handling homographs and context-sensitive phonemes, highlighting their potential for underrepresented languages like Persian.
 
+## Additional Results
+
+After publishing the paper, we conducted further experiments on two additional models: **GPT-4o** and **O1-Preview**. Below is the full table of results from the paper, now including these two additional models as new columns in bold.
+
+| **Metric**               | **llama-3.1**<br>405b-instruct | **gemma2**<br>9b-it | **mixtral**<br>8x7b | **qwen-2**<br>7b-instruct | **mistral**<br>7b-instruct | **gpt-3.5**<br>turbo-instruct | **gpt-4o**<br>mini | **gpt-4** | **claude-3.5**<br>sonnet | **GPT-4o** | **O1-Preview** |
+|--------------------------|-------------------------------|---------------------|---------------------|--------------------------|---------------------------|-------------------------------|-------------------|-----------|--------------------------|------------|----------------|
+| **PER (\%) ↓**           | 8.30                          | 21.58               | 26.84               | 59.06                    | 34.68                     | 11.76                         | 10.44             | 8.28      | 5.80                 | **6.43**   | **9.75**       |
+| **Homograph Acc. (\%) ↑** | 54.00                         | 21.50               | 15.00               | 3.50                     | 12.50                     | 40.50                         | 45.00             | 48.50     | 78.50                | **64.00**  | **64.50**      |
+| **Ezafe F1 (\%) ↑**      | 88.33                         | 61.21               | 44.05               | 27.99                    | 38.93                     | 73.04                         | 70.34             | 87.26     | 93.03                | **89.86**  | **85.15**      |
+
 ## Code
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FgWUGkMjnnM4w9jUpZSRuwQlGnqXAhEW?usp=sharing)
+
 The code for the experiments and tools described in the paper is provided in this repository and is accessible in this [colab link](https://colab.research.google.com/drive/1FgWUGkMjnnM4w9jUpZSRuwQlGnqXAhEW?usp=sharing).
 
 
@@ -19,15 +31,17 @@ The code for the experiments and tools described in the paper is provided in thi
 - **[Sentence-Bench](https://huggingface.co/datasets/MahtaFetrat/SentenceBench)**: Benchmarking dataset for sentence-level G2P evaluation in Persian.
 - **[Kaamel-Dict](https://huggingface.co/datasets/MahtaFetrat/KaamelDict)**: Open-source Persian G2P dictionary with over 120,000 entries.
 
-## Paper and Citation (TO BE UPDATED)
-You can access the paper [here](https://arxiv.org/abs/2409.08554). If you use this work, please cite it as follows:
+## Paper and Citation
+You can access the paper [here](https://ieeexplore.ieee.org/abstract/document/10888370). If you use this work, please cite it as follows:
 
 ```
-@article{qharabagh2024llm,
+@inproceedings{qharabagh2025llm,
   title={LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study},
   author={Qharabagh, Mahta Fetrat and Dehghanian, Zahra and Rabiee, Hamid R},
-  journal={arXiv preprint arXiv:2409.08554},
-  year={2024}
+  booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={1--5},
+  year={2025},
+  organization={IEEE}
 }
 ```