NoisyLLM

Overview

NoisyLLM is a comprehensive toolkit designed for robust fine-tuning of large language models on noisy training data. It provides techniques and strategies for handling data quality issues, label noise, and distribution shifts to maintain model performance despite imperfect training data.

Key Features

Noise-Robust Training: Techniques for training on noisy data
Label Noise Handling: Methods for dealing with incorrect labels
Data Filtering: Intelligent filtering of low-quality samples
Confidence Estimation: Estimate sample quality and confidence
Curriculum Learning: Progressive training strategies
Robustness Evaluation: Comprehensive evaluation on noisy data

Technical Implementation

Noise Handling Techniques

Sample Weighting: Assign weights based on sample quality
Confidence Learning: Learn from noisy labels
Data Cleaning: Identify and filter noisy samples
Augmentation: Robust data augmentation strategies
Regularization: Techniques to prevent overfitting to noise
Ensemble Methods: Combine multiple models for robustness

Training Framework

Noise-aware loss functions
Sample filtering strategies
Confidence estimation
Progressive training
Validation on clean data
Robustness testing

Key Capabilities

Training on noisy datasets
Label noise detection and correction
Sample quality estimation
Robust fine-tuning strategies
Curriculum learning support
Comprehensive evaluation
Noise robustness metrics
Detailed analysis tools

Code Repository

Explore the implementation on GitHub:

git clone https://github.com/Kernel-ML/noisyllm.git
cd noisyllm
pip install -e .
noisyllm train --model bert-base-uncased --data noisy_data.json

Use Cases

Fine-tuning on crowdsourced data
Learning from user-generated content
Handling mislabeled datasets
Domain adaptation with noisy data
Robust model training
Data quality improvement

Future Enhancements

Advanced noise detection algorithms
Multi-task learning support
Real-time noise filtering
Enhanced confidence estimation
Automated data cleaning