rbg-research/EMNLP-2025
Domain:
natural language processingCreator:
rbg
Host:
RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages
# EMNLP-2025
RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages
## Contents
| S.No | Division | Description | Link |
|:----:|:---------------------:|:----------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|
| 1 | Model Selection | Qualitative benchmarking against multilingual translation (NLLB, MADLAD-400) | Link |
| 2 | Zero-Shot Inference | Zero-Shot Inference with MADLAD-400 | Link |
| 3 | Result and Analysis | Analysis on the results of WMT 2025 Training Corpus | Link |
```commandline
.
├── data
│ ├── README.md
│ └── WMT_INDIC_MT_Task_2025.zip
├── LICENSE
├── notebooks
│ ├── 0.Model-Selection.ipynb
│ ├── 1.Zero-shot-Inference-Training.ipynb
│ ├── 2.Observations.ipynb
│ └── translation_analysis_plots
├── README.md
├── requirements.txt
└── results
├── madlad_translation_batch.csv
└── nllb_translation_eval_combined.csv
```
## Observations
| BLEU Score w.r.t Source Family | BLEU Score w.r.t Target Family |
|:---------------------:|:--------------------------------------------------------------------|
| | |
* The analysis by language family reveals additional nuances in the zero-shot translation performance patterns.
As a source language family, Indo-Aryan achieves the highest mean BLEU score, with a wide spread indicating both strong
successes (e.g., Assamese→English) and weaker cases.
* Tibeto-Burman and Indo-European source families achieve more modest but consistent BLEU scores, while Austroasiatic
scores are notably lower, reflecting the challenges in leveraging these …