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Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Kexin Ma, Ruochun Jin, Xi Wang, Huan Chen, Jing Ren, Yuhua Tang

TL;DR

This work tackles data quality issues in Retrieval-Augmented LLMs (RALMs) by introducing Context Matching Dependencies (CMDs), a principled rule-based framework that enforces consistency between retrieved contexts and their semantic meaning. The Context-Driven Index Trimming (CDIT) framework leverages CMDs and LLMs to prune inconsistent retrievals and to trim vector database indices, thereby improving downstream answer accuracy. Empirical results across diverse datasets and models show average improvements of roughly 3–5% with peaks up to 15.21%, demonstrated across multiple indexing schemes and even when integrated with Self-RAG. While effective, the approach notes limitations in handling long texts, relies on GPT-based judgments, and encourages future CMD mining and offline alternatives to reduce dependence on online LLMs.

Abstract

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses.However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts.Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality.Experiments demonstrate on challenging question-answering tasks.Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly.

Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

TL;DR

This work tackles data quality issues in Retrieval-Augmented LLMs (RALMs) by introducing Context Matching Dependencies (CMDs), a principled rule-based framework that enforces consistency between retrieved contexts and their semantic meaning. The Context-Driven Index Trimming (CDIT) framework leverages CMDs and LLMs to prune inconsistent retrievals and to trim vector database indices, thereby improving downstream answer accuracy. Empirical results across diverse datasets and models show average improvements of roughly 3–5% with peaks up to 15.21%, demonstrated across multiple indexing schemes and even when integrated with Self-RAG. While effective, the approach notes limitations in handling long texts, relies on GPT-based judgments, and encourages future CMD mining and offline alternatives to reduce dependence on online LLMs.

Abstract

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses.However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts.Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality.Experiments demonstrate on challenging question-answering tasks.Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly.
Paper Structure (21 sections, 1 theorem, 4 equations, 8 figures, 11 tables, 1 algorithm)

This paper contains 21 sections, 1 theorem, 4 equations, 8 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Context Matching Dependencies
  4. Context Driven Index Trimming
  5. Method Overview
  6. Extracting and Comparing Components
  7. Trimming indexes
  8. Experiments
  9. Experimental settings
  10. Baselines
  11. Main Results
  12. Analysis
  13. Conclusion
  14. Implementation
  15. Datasets Details
  16. ...and 6 more sections

Key Result

Theorem 1

Witness Theorem. Given a query $q$ and sentences $s_1, s_2$. If $q[sid]\sim s_1[sid]$ and $q[sid] \not \sim s_2[sid]$, then the query $q$ is a witness to the separation of the two sentences $s1$ and $s_2$.

Figures (8)

  • Figure 1: Improve data quality of database to enhance the accuracy of generated answers by RALMs.
  • Figure 2: Example relational data of natural language
  • Figure 3: Overview of our mechanism. The LLM(a) represents the more advanced large-parameter language models currently, such as GPT-3.5-turbo; LLM(b) stands for LLMs with smaller parameters and easier deployment, such as Llama2-7b, playing the role of a language generator.
  • Figure 4: Diagram of HNSW indexing. A, B, and C denote the data vector and $q_1, q_2, q_3$ denote the query vector. (a) The diagram of HNSW structure. (b) A single-layer graph is extracted from the stereoscopic structure in (a). (c)After trimming the indexes, the relationship pointed to by the dashed line was successfully deleted.
  • Figure 5: Top-k analysis on PopQA with Llama2-7b and IndexL2Flat index structure.
  • ...and 3 more figures

Theorems & Definitions (5)

  • Definition 1
  • Example 1
  • Definition 2
  • Example 2
  • Theorem 1