Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models

TL;DR

This paper introduces a novel retrieval augmented generation method that enhances factual accuracy and evidence utilization in large language models by coordinating semantic alignment with evidence constraints.

Contribution

It proposes a unified framework that aligns retrieved evidence semantically with generation goals and explicitly constrains evidence use, improving factual reliability.

Findings

Improved factual accuracy and verifiability in generation results.

Stable enhancements across multiple quality metrics.

Effective coordination of semantic alignment and evidence constraints.

Abstract

Retrieval augmented generation mitigates limitations of large language models in factual consistency and knowledge updating by introducing external knowledge. However, practical applications still suffer from semantic misalignment between retrieved results and generation objectives, as well as insufficient evidence utilization. To address these challenges, this paper proposes a retrieval augmented generation method that integrates semantic alignment with evidence constraints through coordinated modeling of retrieval and generation stages. The method first represents the relevance between queries and candidate evidence within a unified semantic space. This ensures that retrieved results remain semantically consistent with generation goals and reduces interference from noisy evidence and semantic drift. On this basis, an explicit evidence constraint mechanism is introduced. Retrieved evidence is transformed from an implicit context into a core control factor in generation. This restricts the expression scope of generated content and strengthens dependence on evidence. By jointly modeling semantic consistency and evidence constraints within a unified framework, the proposed approach improves factual reliability and verifiability while preserving natural language fluency. Comparative results show stable improvements across multiple generation quality metrics. This confirms the effectiveness and necessity of coordinated semantic alignment and evidence constraint modeling in retrieval augmented generation tasks.