Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model
Generating high-quality answers consistently by providing contextual information embedded in the prompt passed to the Large Language Model (LLM) is dependent on the quality of information retrieval. As the corpus of contextual information grows, the answer/inference quality of Retrieval Augmented Generation (RAG) based Question Answering (QA) systems declines. This work solves this problem by combining classical text classification with the Large Language Model (LLM) to enable quick information retrieval from the vector store and ensure the relevancy of retrieved information. For the same, this work proposes a new approach Context Augmented retrieval (CAR), where partitioning of vector database by real-time classification of information flowing into the corpus is done. CAR demonstrates good quality answer generation along with significant reduction in information retrieval and answer generation time.
Code (0)
등록된 구현이 없습니다.
Tasks
Answer GenerationInformation RetrievalLanguage ModelingLanguage ModellingLarge Language ModelQuestion AnsweringRAGResponse GenerationRetrievalRetrieval-augmented Generationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval module is typically evaluated with relevanc…
DiagnosticFormRAGRetrieval+1RAGAS: Automated Evaluation of Retrieval Augmented Generation
We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are composed of a retrieval and an LLM based genera…
RAGRetrievalRetrieval-augmented GenerationFastRAG: Retrieval Augmented Generation for Semi-structured Data
Efficiently processing and interpreting network data is critical for the operation of increasingly complex networks. Recent advances in Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques have…
ManagementQuestion AnsweringRAGRetrieval+1Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm
As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval accuracy and computational efficiency. This…
Computational EfficiencyLightRAG: Simple and Fast Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, exi…
Information RetrievalRAGRetrievalRetrieval-augmented Generation