Latest Research Papers
2024-09-12
arXiv
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
This paper benchmarks and evaluates various ranking models for enhancing the accuracy of text retrieval in question-answering tasks, introducing a new model, NV-RerankQA-Mistral-4B-v3, that significantly improves accuracy. It also discusses the trade-offs between model size, accuracy, and system requirements in real-world applications.
Ranking models play a crucial role in enhancing overall accuracy of text
retrieval systems. These multi-stage systems typically utilize either dense
embedding models or sparse lexical indices to retrieve relevant passages based
on a given query, followed by ranking models that refine the ordering of the
candidate passages by its relevance to the query.
This paper benchmarks various publicly available ranking models and examines
their impact on ranking accuracy. We focus on text retrieval for
question-answering tasks, a common use case for Retrieval-Augmented Generation
systems. Our evaluation benchmarks include models some of which are
commercially viable for industrial applications.
We introduce a state-of-the-art ranking model, NV-RerankQA-Mistral-4B-v3,
which achieves a significant accuracy increase of ~14% compared to pipelines
with other rerankers. We also provide an ablation study comparing the
fine-tuning of ranking models with different sizes, losses and self-attention
mechanisms.
Finally, we discuss challenges of text retrieval pipelines with ranking
models in real-world industry applications, in particular the trade-offs among
model size, ranking accuracy and system requirements like indexing and serving
latency / throughput.
2024-07-01
arXiv
Searching for Best Practices in Retrieval-Augmented Generation
The paper investigates different RAG approaches to identify optimal practices that balance performance and efficiency, and shows that multimodal retrieval techniques can enhance question-answering and content generation.
Retrieval-augmented generation (RAG) techniques have proven to be effective
in integrating up-to-date information, mitigating hallucinations, and enhancing
response quality, particularly in specialized domains. While many RAG
approaches have been proposed to enhance large language models through
query-dependent retrievals, these approaches still suffer from their complex
implementation and prolonged response times. Typically, a RAG workflow involves
multiple processing steps, each of which can be executed in various ways. Here,
we investigate existing RAG approaches and their potential combinations to
identify optimal RAG practices. Through extensive experiments, we suggest
several strategies for deploying RAG that balance both performance and
efficiency. Moreover, we demonstrate that multimodal retrieval techniques can
significantly enhance question-answering capabilities about visual inputs and
accelerate the generation of multimodal content using a "retrieval as
generation" strategy.