re2g reranker nq

Introduction

Re2G (Retrieve, Rerank, Generate) is a model developed by IBM for reranking passage results in information retrieval tasks. It leverages neural initial retrieval and reranking to enhance the accuracy of natural language generation systems. This model is particularly useful for tasks requiring a large amount of knowledge with efficient computational resource use.

Architecture

Re2G builds on existing models like RAG and REALM by incorporating both neural initial retrieval and reranking into a sequence-to-sequence generation framework. It uses BART as the base architecture to combine retrieval results from different sources, such as BM25 and neural retrieval, allowing for enhanced passage ranking. This integration permits handling incomparable scores from different retrieval methods.

Training

The training process for Re2G involves a unique knowledge distillation approach, which trains the retrieval, reranker, and generation components using only the target sequence's ground truth. This method enables significant performance improvements across various tasks, including zero-shot slot filling, question answering, fact-checking, and dialogue.

Guide: Running Locally

1. Clone Repository: Clone the GitHub repository from IBM's GitHub.
2. Set Up Environment: Ensure your environment has Python and necessary libraries installed. This can typically be achieved using a virtual environment.
3. Run Script: Use reranker_apply.py for applying the reranker. Modify paths and parameters as needed for your specific use case.
4. Cloud GPUs: For optimal performance, especially with larger datasets or real-time applications, consider using cloud-based GPUs (e.g., AWS, Google Cloud, or Azure).

License

The Re2G model is licensed under the Apache 2.0 License, allowing for broad use and modification, provided the terms of the license are followed.