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

• URL: http://arxiv.org/abs/2603.04647v1
• Date: Wed, 04 Mar 2026 22:21:04 GMT
• Title: Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models
• Authors: Xin Chen, Saili Uday Gadgil, Jiarong Qiu,
• Abstract summary: This paper proposes a retrieval augmented generation method that integrates semantic alignment with evidence constraints.<n>It improves factual reliability and verifiability while preserving natural language fluency.
• Score: 4.023398871264227
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval [60.25608870901428]
  Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs)<n>We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source robustness.
  arXiv  Detail & Related papers  (2026-03-05T18:42:51Z)
• Reasoning-Driven Amodal Completion: Collaborative Agents and Perceptual Evaluation [17.405818788700234]
  We present a Collaborative Multi-Agent Reasoning Framework that explicitly decouples Semantic Planning from Visual Synthesis.<n>Our method generates a structured, explicit plan before pixel generation, enabling visually and semantically coherent single-pass synthesis.<n>Addressing the limitations of traditional metrics in assessing inferred invisible content, we introduce the MAC-Score, a novel human-aligned evaluation metric.
  arXiv  Detail & Related papers  (2025-12-24T04:39:45Z)
• LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints [5.2604064919135896]
  This paper addresses the issues of insufficient coverage, unstable results, and limited reliability in retrieval-augmented generation under complex knowledge environments.<n>It proposes a confidence control method that integrates multi-granularity memory indexing with uncertainty estimation.<n>The results show that the method achieves superior performance over existing models in QA accuracy, retrieval recall, ranking quality, and factual consistency.
  arXiv  Detail & Related papers  (2025-10-30T23:48:37Z)
• Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models [9.664217498808338]
  We introduce a unified taxonomy of evidence-based text generation with large language models.<n>We investigate 300 evaluation metrics across seven key dimensions.<n>We highlight open challenges and outline promising directions for future work.
  arXiv  Detail & Related papers  (2025-08-21T09:36:35Z)
• Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement [27.673022970833163]
  We introduce Arg-LLaDA, a novel large language diffusion framework that iteratively improves summaries.<n>Our method combines a flexible masking controller with a sufficiency-checking module to identify and revise unsupported, redundant, or incomplete spans.<n> Empirical results on two benchmark datasets demonstrate that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 automatic evaluation metrics.
  arXiv  Detail & Related papers  (2025-07-25T09:07:52Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor [4.35807211471107]
  This work proposes a novel two-stage consistency learning approach for retrieved information compression in retrieval-augmented language models. The proposed method is empirically validated across multiple datasets, demonstrating notable enhancements in precision and efficiency for question-answering tasks.
  arXiv  Detail & Related papers  (2024-06-04T12:43:23Z)
• Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization [56.94741578760294]
  We propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summary. Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact.
  arXiv  Detail & Related papers  (2023-05-23T22:11:47Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)
• Evaluating Factuality in Generation with Dependency-level Entailment [57.5316011554622]
  We propose a new formulation of entailment that decomposes it at the level of dependency arcs. We show that our dependency arc entailment model trained on this data can identify factual inconsistencies in paraphrasing and summarization better than sentence-level methods.
  arXiv  Detail & Related papers  (2020-10-12T06:43:10Z)
• A Controllable Model of Grounded Response Generation [122.7121624884747]
  Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process. We propose a framework that we call controllable grounded response generation (CGRG) We show that using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.
  arXiv  Detail & Related papers  (2020-05-01T21:22:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.