PDF available on arXiv

InSQuaD is a research framework for efficient in-context learning that leverages submodular mutual information to optimize the quality-diversity tradeoff in example selection for large language models. This implementation supports various retrieval methods, subset selection strategies, and generative models for comprehensive evaluation across multiple datasets.

• Submodular Optimization: Implementation of facility location and graph cut losses for quality-diversity tradeoffs
• Multiple Retrieval Methods: Support for semantic search models (MPNet, sentence transformers) and dense indexes (FAISS)
• Diverse Datasets: Pre-configured loaders for MRPC, SST, MNLI, DBPedia, RTE, HellaSwag, XSum, MultiWOZ, and GeoQ
• Flexible Architecture: Modular design supporting various generative models (OpenAI, HuggingFace transformers)
• Comprehensive Evaluation: Built-in metrics and analysis tools for experimental evaluation
• Experiment Management: YAML-based configuration system with Weights & Biases integration

• Python 3.9+
• CUDA-compatible GPU (recommended)
• Required API keys (OpenAI, Weights & Biases)

1. Clone the repository:

   git clone https://github.com/Ghost---Shadow/InSQuaD.git
   cd InSQuaD

2. Create conda environment (recommended):

   conda create -n InSQuaD python=3.9 -y
   conda activate InSQuaD

3. Install dependencies:

   ./devops/install.sh

4. Set up environment variables: Create a .env file in the root directory with your API keys:

   OPENAI_API_KEY=your_openai_key_here
   WANDB_API_KEY=your_wandb_key_here

1. Single experiment:

   python src/train.py experiments/tests/InSQuaD_test_experiment.yaml

2. Full experiment suite:

   sh run_all_experiments.sh

3. Offline evaluation:

   python src/offline_eval.py path/to/experiment/config.yaml

See experiments/ directory for configuration examples.

Run the test suite to ensure everything is working correctly:

# Test everything (some tests may fail on Windows)
python -m unittest discover -s src -p "*_test.py"

# Test specific modules
python -m unittest discover -s src.dataloaders -p "*_test.py"
python -m unittest discover -s src.dense_indexes -p "*_test.py"
python -m unittest discover -s src.shortlist_strategies -p "*_test.py"
python -m unittest discover -s src.subset_selection_strategies -p "*_test.py"

Format code using Black:

black .

src/
├── dataloaders/          # Dataset loading and preprocessing
├── dense_indexes/        # FAISS and other dense retrieval indexes  
├── generative_models/    # LLM wrappers (OpenAI, HuggingFace)
├── losses/              # Submodular loss functions
├── semantic_search_models/ # Embedding models
├── shortlist_strategies/ # Example selection strategies
├── subset_selection_strategies/ # Submodular optimization
└── training_strategies/  # Training loops and algorithms

• MRPC: Microsoft Research Paraphrase Corpus
• SST: Stanford Sentiment Treebank (binary and 5-class)
• MNLI: Multi-Genre Natural Language Inference
• DBPedia: Database entity classification
• RTE: Recognizing Textual Entailment
• HellaSwag: Commonsense reasoning
• XSum: Extractive summarization
• MultiWOZ: Task-oriented dialogue
• GeoQ: Geographic question answering

• OpenAI GPT models (GPT-3.5, GPT-4)
• HuggingFace transformers (Gemma, T5, etc.)
• Custom model implementations

• MPNet (all-mpnet-base-v2)
• Sentence Transformers
• Custom embedding models

The framework includes comprehensive analysis tools:

• Performance Tables: Automated LaTeX table generation
• Visualization: Plotting utilities for results analysis
• Statistical Analysis: Confidence intervals and significance tests
• Time Analysis: Efficiency comparisons across methods

Results are automatically logged to Weights & Biases for easy tracking and comparison.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your code follows the existing style and includes appropriate tests.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

If you use this code in your research, please cite:

@inproceedings{insquad2025,
  title={InSQuaD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity},
  author={Nanda, Souradeep and Majee, Anay and Iyer, Rishab Krishnan},
  booktitle={Proceedings of the 2025 IEEE International Conference on Data Mining (ICDM)},
  year={2025},
  organization={IEEE},
  url={https://github.com/Ghost---Shadow/InSQuaD}
}

For questions, issues, or feature requests, please open an issue on GitHub or contact the maintainers.