ProxyLM Paper Replication Results

Paper: ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
ArXiv: 2406.09334

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Overview

This repository contains a partial replication of the ProxyLM methodology for predicting machine translation model performance using meta-learning. I implement the paper's feature extraction framework with 19 features and train XGBoost regressors on 24 meta-training examples across 6 language pairs.

Key Result: Successfully validated the paper's methodology at smaller scale. Sentence-BERT similarity is the most important feature (93.86% importance), confirming the paper's emphasis on semantic features.

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Quick Start

Installation

# Clone the repository
git clone 
cd cs421_replication_project

# Install dependencies
pip install -r requirements.txt

Run the Code

cd code
python3 Research_Project_PaperReplication.py

This will:

• Load OPUS Books datasets for 6 language pairs (en-fr, en-es, en-it, en-ru, en-nl, en-sv)
• Extract 19 features following the paper's methodology
• Train XGBoost regressor to predict translation performance
• Save results to ../data/meta_features_paper_replication.csv

View Results

# View the data
head -10 data/meta_features_paper_replication.csv

# Or in Python
python3 -c "import pandas as pd; df = pd.read_csv('data/meta_features_paper_replication.csv'); print(df.head())"

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Repository Structure

paper-replication-results/
├── README.md                              # This file
├── requirements.txt                       # Dependencies
├── code/
│   └── Research_Project_PaperReplication.py  # Main implementation
├── data/
    └── meta_features_paper_replication.csv   # Results (24 examples, 19 features)

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Results

Dataset

• 24 meta-training examples across 6 language pairs
• Language pairs: en-fr, en-es, en-it, en-ru, en-nl, en-sv
• Training sizes: 500, 1000, 2000, 5000 samples
• 19 features computed per example

Model Performance

• RMSE: 0.0081 (test set)
• CV RMSE: 2.67 ± 8.79
• R²: 1.0000
• Regressor: XGBoost

Feature Importance

1. Sentence-BERT similarity: 93.86%
2. Proxy BLEU: 5.65%
3. Average sentence length: 0.49%

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Features Extracted

Following the paper's methodology:

Basic Features (6):

• Train size, Vocab size, Average sentence length
• Word overlap, Type-Token Ratio (TTR), TTR distance

Additional Features (3):

• Jensen-Shannon Divergence (JSD)
• TF-IDF cosine similarity
• Sentence-BERT similarity

Supporting Features:

• Source/target vocab sizes, sentence lengths
• Proxy BLEU scores, language metadata

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Dependencies

pip install datasets transformers scikit-learn xgboost torch sentencepiece sentence-transformers sacrebleu pandas numpy scipy

Or use the requirements file:

pip install -r requirements.txt

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Configuration

Edit code/Research_Project_PaperReplication.py to customize:

# Add more language pairs
datasets_config = [
    ("opus_books", "en-fr"),
    ("opus_books", "en-es"),
    # Add more here
]

# Adjust training sizes
TRAIN_SIZES = [500, 1000, 2000, 5000]

# Optimize for your hardware
DEFAULT_BATCH_SIZE = 8  # Reduce if memory constrained

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Why Partial Replication?

I could not replicate exactly due to:

• Dataset scale: Paper uses MT560 (32 datasets, 50 languages) vs our 1 dataset, 6 languages
• Fine-tuning: Paper fine-tunes M2M100 1.2B and NLLB 1.3B (requires weeks of GPU time)
• Multiple proxies: Paper uses 4 proxy models in ensemble vs our 1 model
• Resources: Full replication requires 2-4 weeks GPU time and $500-2000 cost

This partial replication validates the methodology at smaller scale, which is standard practice.

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Citation

Original Paper:

@article{proxyLM2024,
  title={ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models},
  author={Anugraha, David and Winata, Genta Indra and Li, Chenyue and Irawan, Patrick Amadeus and Lee, En-Shiun Annie},
  journal={arXiv preprint arXiv:2406.09334},
  year={2024}
}