Enhancing Retrieval Processes for Language Generation with Augmented Queries
This work addresses retrieval challenges for language generation to enhance accuracy in smart technology applications, but it appears incremental as it builds on existing RAG methods with optimizations.
This research tackled the problem of inaccuracies like hallucination in language models by using Retrieval-Augmented Generation (RAG) with query optimization, showing significant performance improvements, particularly with prompt augmenters, and demonstrating consistency across encodings and efficient use of resources with the Orca2 7B model.
In the rapidly changing world of smart technology, searching for documents has become more challenging due to the rise of advanced language models. These models sometimes face difficulties, like providing inaccurate information, commonly known as "hallucination." This research focuses on addressing this issue through Retrieval-Augmented Generation (RAG), a technique that guides models to give accurate responses based on real facts. To overcome scalability issues, the study explores connecting user queries with sophisticated language models such as BERT and Orca2, using an innovative query optimization process. The study unfolds in three scenarios: first, without RAG, second, without additional assistance, and finally, with extra help. Choosing the compact yet efficient Orca2 7B model demonstrates a smart use of computing resources. The empirical results indicate a significant improvement in the initial language model's performance under RAG, particularly when assisted with prompts augmenters. Consistency in document retrieval across different encodings highlights the effectiveness of using language model-generated queries. The introduction of UMAP for BERT further simplifies document retrieval while maintaining strong results.