---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23
datasets:
- OpenLLM-Ro/ro_dpo_helpsteer
- OpenLLM-Ro/ro_dpo_ultrafeedback
- OpenLLM-Ro/ro_dpo_magpie
- OpenLLM-Ro/ro_dpo_argilla_magpie
- OpenLLM-Ro/ro_dpo_helpsteer2
model-index:
    - name: OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23
      results:
        - task:
            type: text-generation
          dataset:
            name: RoMT-Bench
            type: RoMT-Bench
          metrics:
            - name: Score
              type: Score
              value: 7.00
        - task:
            type: text-generation
          dataset:
            name: RoCulturaBench
            type: RoCulturaBench
          metrics:
            - name: Score
              type: Score
              value: 4.73
        - task:
            type: text-generation
          dataset:
            name: Romanian_Academic_Benchmarks
            type: Romanian_Academic_Benchmarks
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 53.76
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_arc_challenge
            type: OpenLLM-Ro/ro_arc_challenge
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 51.09
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_mmlu
            type: OpenLLM-Ro/ro_mmlu
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 56.22
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_winogrande
            type: OpenLLM-Ro/ro_winogrande
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 66.77
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_hellaswag
            type: OpenLLM-Ro/ro_hellaswag
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 59.38
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_gsm8k
            type: OpenLLM-Ro/ro_gsm8k
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 31.54
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_truthfulqa
            type: OpenLLM-Ro/ro_truthfulqa
          metrics:
            - name: Average accuracy
              type: accuracy
              value: 57.56
        - task:
            type: text-generation
          dataset:
            name: LaRoSeDa_binary
            type: LaRoSeDa_binary
          metrics:
            - name: Average macro-f1
              type: macro-f1
              value: 96.87
        - task:
            type: text-generation
          dataset:
            name: LaRoSeDa_multiclass
            type: LaRoSeDa_multiclass
          metrics:
            - name: Average macro-f1
              type: macro-f1
              value: 60.75
        - task:
            type: text-generation
          dataset:
            name: WMT_EN-RO
            type: WMT_EN-RO
          metrics:
            - name: Average bleu
              type: bleu
              value: 20.30
        - task:
            type: text-generation
          dataset:
            name: WMT_RO-EN
            type: WMT_RO-EN
          metrics:
            - name: Average bleu
              type: bleu
              value: 18.57
        - task:
            type: text-generation
          dataset:
            name: XQuAD
            type: XQuAD
          metrics:
            - name: Average exact_match
              type: exact_match
              value: 9.22
        - task:
            type: text-generation
          dataset:
            name: XQuAD
            type: XQuAD
          metrics:
            - name: Average f1
              type: f1
              value: 22.75
        - task:
            type: text-generation
          dataset:
            name: STS
            type: STS
          metrics:
            - name: Average spearman
              type: spearman
              value: 30.82
        - task:
            type: text-generation
          dataset:
            name: STS
            type: STS
          metrics:
            - name: Average pearson
              type: pearson
              value: 20.25
        - task:
            type: text-generation
          dataset:
            name: RoMT-Bench
            type: RoMT-Bench
          metrics:
            - name: First turn
              type: Score
              value: 7.30
            - name: Second turn
              type: Score
              value: 6.70
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_arc_challenge
            type: OpenLLM-Ro/ro_arc_challenge
          metrics:
            - name: 0-shot 
              type: accuracy
              value: 51.59
            - name: 1-shot 
              type: accuracy
              value: 52.10
            - name: 3-shot 
              type: accuracy
              value: 50.99
            - name: 5-shot 
              type: accuracy
              value: 50.81
            - name: 10-shot 
              type: accuracy
              value: 49.70
            - name: 25-shot 
              type: accuracy
              value: 51.33
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_mmlu
            type: OpenLLM-Ro/ro_mmlu
          metrics:
            - name: 0-shot 
              type: accuracy
              value: 56.88
            - name: 1-shot 
              type: accuracy
              value: 55.61
            - name: 3-shot 
              type: accuracy
              value: 56.06
            - name: 5-shot 
              type: accuracy
              value: 56.31
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_winogrande
            type: OpenLLM-Ro/ro_winogrande
          metrics:
            - name: 0-shot 
              type: accuracy
              value: 65.67
            - name: 1-shot 
              type: accuracy
              value: 66.30
            - name: 3-shot 
              type: accuracy
              value: 67.40
            - name: 5-shot 
              type: accuracy
              value: 67.72
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_hellaswag
            type: OpenLLM-Ro/ro_hellaswag
          metrics:
            - name: 0-shot 
              type: accuracy
              value: 60.53
            - name: 1-shot 
              type: accuracy
              value: 60.37
            - name: 3-shot 
              type: accuracy
              value: 58.20
            - name: 5-shot 
              type: accuracy
              value: 58.18
            - name: 10-shot 
              type: accuracy
              value: 59.61
        - task:
            type: text-generation
          dataset:
            name: OpenLLM-Ro/ro_gsm8k
            type: OpenLLM-Ro/ro_gsm8k
          metrics:
            - name: 1-shot 
              type: accuracy
              value: 25.09
            - name: 3-shot 
              type: accuracy
              value: 30.02
            - name: 5-shot 
              type: accuracy
              value: 39.50
        - task:
            type: text-generation
          dataset:
            name: LaRoSeDa_binary
            type: LaRoSeDa_binary
          metrics:
            - name: 0-shot 
              type: macro-f1
              value: 95.39
            - name: 1-shot 
              type: macro-f1
              value: 95.90
            - name: 3-shot 
              type: macro-f1
              value: 98.00
            - name: 5-shot 
              type: macro-f1
              value: 98.17
        - task:
            type: text-generation
          dataset:
            name: LaRoSeDa_multiclass
            type: LaRoSeDa_multiclass
          metrics:
            - name: 0-shot 
              type: macro-f1
              value: 60.30
            - name: 1-shot 
              type: macro-f1
              value: 64.73
            - name: 3-shot 
              type: macro-f1
              value: 58.69
            - name: 5-shot 
              type: macro-f1
              value: 59.30
        - task:
            type: text-generation
          dataset:
            name: WMT_EN-RO
            type: WMT_EN-RO
          metrics:
            - name: 0-shot 
              type: bleu
              value: 5.46
            - name: 1-shot 
              type: bleu
              value: 26.08
            - name: 3-shot 
              type: bleu
              value: 25.90
            - name: 5-shot 
              type: bleu
              value: 23.76
        - task:
            type: text-generation
          dataset:
            name: WMT_RO-EN
            type: WMT_RO-EN
          metrics:
            - name: 0-shot 
              type: bleu
              value: 2.74
            - name: 1-shot 
              type: bleu
              value: 20.95
            - name: 3-shot 
              type: bleu
              value: 31.53
            - name: 5-shot 
              type: bleu
              value: 19.05
        - task:
            type: text-generation
          dataset:
            name: XQuAD_EM
            type: XQuAD_EM
          metrics:
            - name: 0-shot 
              type: exact_match
              value: 12.27
            - name: 1-shot 
              type: exact_match
              value: 17.98
            - name: 3-shot 
              type: exact_match
              value: 5.04
            - name: 5-shot 
              type: exact_match
              value: 1.60
        - task:
            type: text-generation
          dataset:
            name: XQuAD_F1
            type: XQuAD_F1
          metrics:
            - name: 0-shot 
              type: f1
              value: 26.24
            - name: 1-shot 
              type: f1
              value: 32.54
            - name: 3-shot 
              type: f1
              value: 18.00
            - name: 5-shot 
              type: f1
              value: 14.22
        - task:
            type: text-generation
          dataset:
            name: STS_Spearman
            type: STS_Spearman
          metrics:
            - name: 1-shot 
              type: spearman
              value: 76.70
            - name: 3-shot 
              type: spearman
              value: 2.82
            - name: 5-shot 
              type: spearman
              value: 12.95
        - task:
            type: text-generation
          dataset:
            name: STS_Pearson
            type: STS_Pearson
          metrics:
            - name: 1-shot 
              type: pearson
              value: 77.30
            - name: 3-shot 
              type: pearson
              value: -14.56
            - name: 5-shot 
              type: pearson
              value: -1.99

---

# Model Card for Model ID

*Built with Meta Llama 3.1*

This model points/is identical to [RoLlama3.1-8b-Instruct-DPO-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23).

<!-- Provide a quick summary of what the model is/does. -->

RoLlama3.1 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 8B model**. Links to other models can be found at the bottom of this page.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.


- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [RoLlama3.1-8b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23)
- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer), [RoUltraFeedback](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_ultrafeedback), [RoMagpieDPO](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_magpie), [RoArgillaMagpie](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_argilla_magpie), [RoHelpSteer2](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer2)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266

## Intended Use

### Intended Use Cases

RoLlama3.1 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.



## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO")

instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
        {"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."},
        {"role": "user", "content": instruction},
        ]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```

## Academic Benchmarks


<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>Llama-3.1-8B-Instruct</td><td><center>49.87</center></td><td><center>42.86</center></td><td><center>53.73</center></td><td><center>59.71</center></td><td><center>56.82</center></td><td><center>35.56</center></td><td><center>50.54</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>53.03</center></td><td><center>47.69</center></td><td><center>54.57</center></td><td><center>65.84</center></td><td><center>59.94</center></td><td><center><strong>44.30</strong></center></td><td><center>45.82</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>53.36</center></td><td><center>48.97</center></td><td><center>55.17</center></td><td><center>66.52</center></td><td><center><strong>60.73</strong></center></td><td><center>42.03</center></td><td><center>46.71</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>52.74</center></td><td><center>44.84</center></td><td><center>55.06</center></td><td><center>65.87</center></td><td><center>58.67</center></td><td><center>44.17</center></td><td><center>47.82</center></td>
</tr>
<tr>
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>53.76</strong></em></center></td><td><center><em><strong>51.09</strong></em></center></td><td><center><em><strong>56.22</strong></em></center></td><td><center><em><strong>66.77</strong></em></center></td><td><center><em>59.38</em></center></td><td><center><em>31.54</em></center></td><td><center><em><strong>57.56</strong></em></center></td>
</tr>
</tbody>
</table>


  
## Downstream tasks

<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>Llama-3.1-8B-Instruct</td><td><center>95.74</center></td><td><center>59.49</center></td><td><center><strong>98.57</strong></center></td><td><center>82.41</center></td><td><center>19.01</center></td><td><center><strong>27.77</strong></center></td><td><center><strong>29.02</strong></center></td><td><center>39.80</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>94.56</center></td><td><center>60.10</center></td><td><center>95.12</center></td><td><center><strong>87.53</strong></center></td><td><center>21.88</center></td><td><center>23.99</center></td><td><center>28.27</center></td><td><center><strong>40.44</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>95.32</center></td><td><center><strong>60.84</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>23.18</strong></center></td><td><center>25.11</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>96.10</center></td><td><center>55.37</center></td><td><center>-</center></td><td><center>-</center></td><td><center>21.29</center></td><td><center>21.86</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>96.87</strong></em></center></td><td><center><em>60.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>20.30</em></center></td><td><center><em>18.57</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>


<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>Llama-3.1-8B-Instruct</td><td><center><strong>44.96</strong></center></td><td><center><strong>64.45</strong></center></td><td><center><strong>69.50</strong></center></td><td><center><strong>84.31</strong></center></td><td><center>72.11</center></td><td><center>71.64</center></td><td><center>84.59</center></td><td><center>84.96</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>13.59</center></td><td><center>23.56</center></td><td><center>49.41</center></td><td><center>62.93</center></td><td><center>75.89</center></td><td><center>76.00</center></td><td><center><strong>86.86</strong></center></td><td><center><strong>87.05</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>10.74</center></td><td><center>19.75</center></td><td><center>-</center></td><td><center>-</center></td><td><center>73.53</center></td><td><center>74.93</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>21.58</center></td><td><center>36.54</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>78.01</strong></center></td><td><center><strong>77.98</strong></center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em>9.22</em></center></td><td><center><em>22.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>30.82</em></center></td><td><center><em>20.25</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>

## MT-Bench

<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-3.1-8B-Instruct</td><td><center>5.69</center></td><td><center>5.85</center></td><td><center>5.53</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>5.42</center></td><td><center>5.95</center></td><td><center>4.89</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>6.43</center></td><td><center>6.78</center></td><td><center>6.09</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>6.21</center></td><td><center>6.74</center></td><td><center>5.69</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>7.00</strong></em></center></td><td><center><em><strong>7.30</strong></em></center></td><td><center><em><strong>6.70</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
</tbody>
</table>


## RoCulturaBench

<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-3.1-8B-Instruct</td><td><center>3.54</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>3.55</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>4.28</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>4.42</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>4.73</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
</tbody>
</table>



## RoLlama3.1 Model Family

| Model              | Link  |
|--------------------|:--------:|
|RoLlama3.1-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) |
|RoLlama3.1-8b-Instruct-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) |
|RoLlama3.1-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2024-10-09) |
|*RoLlama3.1-8b-Instruct-DPO-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23) |


## Citation 

```
@misc{masala2024vorbecstiromanecsterecipetrain,
      title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, 
      author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
      year={2024},
      eprint={2406.18266},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.18266}, 
}
```
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