Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach

• URL: http://arxiv.org/abs/2607.22584v1
• Date: Mon, 08 Jun 2026 14:09:04 GMT
• Title: Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach
• Abstract summary: This work evaluates a simple and interpretable modification to RAG retrieval ranking that incorporates provenance-informed source reliability priors.<n>The framework is evaluated against a similarity-only baseline on a 120-document health-domain corpus.<n> source-aware reranking improves Precision@5 from 0.48 to 0.72 and reduces average adversarial document retrieval.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility. This work evaluates a simple and interpretable modification to RAG retrieval ranking that incorporates domain-informed source reliability priors. Each document is assigned a prior lambda(s) based on its source type, and retrieval scores are reweighted using score(q, d) = sim(q, d) * lambda(s). The framework is evaluated against a similarity-only baseline on a 120-document health-domain corpus. In this controlled setting, source-aware reranking improves Precision@5 from 0.48 to 0.72 and reduces average adversarial document retrieval under the evaluated threat model, where low-credibility sources are identifiable via metadata. All experiments were executed on Rosie, the high-performance computing cluster at the Milwaukee School of Engineering, which provided the GPU-accelerated infrastructure necessary to run the full experimental pipeline reliably and reproducibly. These results suggest a potential mitigation strategy for source quality degradation in RAG pipelines, within the limits of the experimental setup described.

Related papers

• Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation [95.55552104238787]
  Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains.<n>Traditional TTA methods become ineffective when the label distribution shift occurs.<n>We introduce a new method named Reliable Neural Collapse approximation (ReNC) for Open-World Test-Time Adaptation.
  arXiv  Detail & Related papers  (2026-08-20T10:57:44Z)
• ContextNest: Verifiable Context Governance for Autonomous AI Agent [0.0]
  We present ContextNest, an open specification and reference implementation for governed AI-consumable knowledge vaults.<n>It supplies the governance layer beneath retrieval, determining which artifacts are approved, current, attributable, and integrity-verified.<n>We report first empirical results from two controlled experiments.
  arXiv  Detail & Related papers  (2026-07-02T12:51:38Z)
• Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG [6.324418485299426]
  Large language models may still prioritize parametric knowledge over retrieved information when conflicts arise.<n>We propose a novel training-free decoding framework, emphGrounded Decoding, to improve factual consistency in RAG without modifying model parameters.<n>Our results indicate that probability-level fusion provides a strong and efficient alternative to logit-level intervention methods for faithful RAG decoding.
  arXiv  Detail & Related papers  (2026-05-29T23:47:39Z)
• Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
  Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
  arXiv  Detail & Related papers  (2026-05-29T03:52:44Z)
• Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding [64.98167175790817]
  We propose ConSUM that reranks candidate summaries by considering two factors: consistency to the source document and consensus among the other candidates.<n>Our system is competitive with existing methods, with human evaluations confirming that its generated summaries are preferred over those from other systems.
  arXiv  Detail & Related papers  (2026-05-28T04:14:43Z)
• RaV-IDP: A Reconstruction-as-Validation Framework for Faithful Intelligent Document Processing [0.0]
  Reconstruction as Validation (RaV-IDP) is a document processing pipeline that introduces reconstruction as a first-class architectural component.<n>A dedicated reconstructor renders the extracted representation back into a form comparable to the original document region.<n>When fidelity falls below a per-entity-type threshold, a structured GPT-4.1 vision fallback is triggered and the validation loop repeats.
  arXiv  Detail & Related papers  (2026-04-26T10:26:42Z)
• Open-Source Reproduction and Explainability Analysis of Corrective Retrieval Augmented Generation [0.0]
  We present a fully open-source reproduction of Corrective Retrieval Augmented Generation (CRAG)<n>CRAG improves robustness of RAG systems by evaluating retrieved document quality and triggering corrective actions.<n>We replace proprietary web search with the Wikipedia API and the original LLaMA-2 generator with Phi-3-mini-4k-instruct.
  arXiv  Detail & Related papers  (2026-03-17T06:38:00Z)
• SimGR: Escaping the Pitfalls of Generative Decoding in LLM-based Recommendation [68.00727783181289]
  A core objective in recommender systems is to accurately model the distribution of user preferences over items to enable personalized recommendations.<n>We observe that existing methods inevitably introduce systematic bias when estimating item-level preference distributions.<n>We propose textbfSimply textbfGenerative textbfRecommendation (textbfSimGR), a framework that directly models item-level preference distributions in a shared latent space.
  arXiv  Detail & Related papers  (2026-02-08T07:26:52Z)
• Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations [49.671779378073886]
  We study question answering in the domain of radio regulations.<n>We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline.<n>Our approach consistently improves generation accuracy across all tested models.
  arXiv  Detail & Related papers  (2025-09-11T17:43:42Z)
• Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains [13.58151841630302]
  We propose a novel method METEORA that replaces re-ranking in RAG with a rationale-driven selection approach.<n>We show METEORA improves generation accuracy by 33.34% while using approximately 50% fewer chunks than state-of-the-art re-ranking methods.<n>In adversarial settings, METEORA significantly improves the F1 score from 0.10 to 0.44.
  arXiv  Detail & Related papers  (2025-05-21T20:57:16Z)
• Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport [84.51977664336056]
  We propose a novel scalable approach for estimating the robust continuous barycenter.<n>Our method is framed as a min-max optimization problem and is adaptable to general cost functions.
  arXiv  Detail & Related papers  (2024-10-04T23:27:33Z)
• Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection [52.355018626115346]
  Building object detectors that are robust to domain shifts is critical for real-world applications. We propose to use Relative Gradient Norm as a way to measure the vulnerability of a backbone to feature distortion. We present recipes to boost OOD robustness for both types of backbones.
  arXiv  Detail & Related papers  (2023-03-26T14:50:43Z)

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.