mlsanityData to 7 Oct 2026

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

arXiv 2603.04647 ↗First posted 4 Mar 2026cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 17 of its 27 references that are in the map. First posted on arXiv 4 Mar 2026.

Builds on

17 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration7 Apr

Other references · 16

  1. 2024Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAGcited by 67
  2. 2024BioRAG: A RAG-LLM Framework for Biological Question Reasoningcited by 42
  3. 2024T-RAG: Lessons from the LLM Trenchescited by 23
  4. 2025U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystackcited by 20
  5. 2024Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadatacited by 16
  6. 2023Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generationcited by 14
  7. 2024Enhanced Multimodal RAG-LLM for Accurate Visual Question Answeringcited by 10
  8. 2025Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Modelscited by 10
  9. 2025Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Modelscited by 7
  10. 2025RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedbackcited by 5
  11. 2025Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Modelscited by 5
  12. 2025Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Modelscited by 4
4 more
  1. 2025Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Makingcited by 3
  2. 2025Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Modelscited by 3
  3. 2025Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Modelscited by 2
  4. 2025Advancing Text Classification with Large Language Models and Neural Attention Mechanismscited by 1

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  1. 2026ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
  2. 2026Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning
  3. 2026A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems
  4. 2024RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation
  5. 2025Aligning Extraction and Generation for Robust Retrieval-Augmented Generation
  6. 2026Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
  7. 2025Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
  8. 2025RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence