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Semantic Tokens in Retrieval Augmented Generation

Joel Suro

TL;DR

The paper addresses the reliability of Retrieval-Augmented Generation (RAG) systems as data access expands, noting that probabilistic outputs of large language models can lead to erroneous responses. It proposes a Comparative RAG framework that introduces an Evaluator module to align retrieved chunks with external deterministic reasoning via chunk hashing and standardized chunking, bridging probabilistic and deterministic components. The approach is designed to be compatible with a range of RAG architectures (including GraphRAG) and aims to produce deterministically grounded, higher-precision answers. This methodology has practical implications for deploying RAG-based systems in high-stakes domains requiring verifiability and scalability.

Abstract

Retrieval-Augmented Generation (RAG) architectures have recently garnered significant attention for their ability to improve truth grounding and coherence in natural language processing tasks. However, the reliability of RAG systems in producing accurate answers diminishes as the volume of data they access increases. Even with smaller datasets, these systems occasionally fail to address simple queries. This issue arises from their dependence on state-of-the-art large language models (LLMs), which can introduce uncertainty into the system's outputs. In this work, I propose a novel Comparative RAG system that introduces an evaluator module to bridge the gap between probabilistic RAG systems and deterministically verifiable responses. The evaluator compares external recommendations with the retrieved document chunks, adding a decision-making layer that enhances the system's reliability. This approach ensures that the chunks retrieved are both semantically relevant and logically consistent with deterministic insights, thereby improving the accuracy and overall efficiency of RAG systems. This framework paves the way for more reliable and scalable question-answering applications in domains requiring high precision and verifiability.

Semantic Tokens in Retrieval Augmented Generation

TL;DR

The paper addresses the reliability of Retrieval-Augmented Generation (RAG) systems as data access expands, noting that probabilistic outputs of large language models can lead to erroneous responses. It proposes a Comparative RAG framework that introduces an Evaluator module to align retrieved chunks with external deterministic reasoning via chunk hashing and standardized chunking, bridging probabilistic and deterministic components. The approach is designed to be compatible with a range of RAG architectures (including GraphRAG) and aims to produce deterministically grounded, higher-precision answers. This methodology has practical implications for deploying RAG-based systems in high-stakes domains requiring verifiability and scalability.

Abstract

Retrieval-Augmented Generation (RAG) architectures have recently garnered significant attention for their ability to improve truth grounding and coherence in natural language processing tasks. However, the reliability of RAG systems in producing accurate answers diminishes as the volume of data they access increases. Even with smaller datasets, these systems occasionally fail to address simple queries. This issue arises from their dependence on state-of-the-art large language models (LLMs), which can introduce uncertainty into the system's outputs. In this work, I propose a novel Comparative RAG system that introduces an evaluator module to bridge the gap between probabilistic RAG systems and deterministically verifiable responses. The evaluator compares external recommendations with the retrieved document chunks, adding a decision-making layer that enhances the system's reliability. This approach ensures that the chunks retrieved are both semantically relevant and logically consistent with deterministic insights, thereby improving the accuracy and overall efficiency of RAG systems. This framework paves the way for more reliable and scalable question-answering applications in domains requiring high precision and verifiability.

Paper Structure

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Comparative RAG
  3. Evaluator
  4. Example: Food Delivery Scenario
  5. Conclusion

Figures (1)

  • Figure 1: Comparative RAG