# Structured Relevance Assessment for Robust Retrieval-Augmented Language Models

arXiv: https://arxiv.org/abs/2507.21287 · HTML: https://mlsanity.com/paper/2507.21287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 8 of 8)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Faithful Reasoning Using Large Language Models](https://mlsanity.com/paper/2208.14271/) (2022)

## Abstract (arXiv)

Retrieval-Augmented Language Models (RALMs) face significant challenges in reducing factual errors, particularly in document relevance evaluation and knowledge integration. We introduce a framework for structured relevance assessment that enhances RALM robustness through improved document evaluation, balanced intrinsic and external knowledge integration, and effective handling of unanswerable queries. Our approach employs a multi-dimensional scoring system that considers both semantic matching and source reliability, utilizing embedding-based relevance scoring and synthetic training data with mixed-quality documents. We implement specialized benchmarking on niche topics, a knowledge integration mechanism, and an "unknown" response protocol for queries with insufficient knowledge coverage. Preliminary evaluations demonstrate significant reductions in hallucination rates and improved transparency in reasoning processes. Our framework advances the development of more reliable question-answering systems capable of operating effectively in dynamic environments with variable data quality. While challenges persist in accurately distinguishing credible information and balancing system latency with thoroughness, this work represents a meaningful step toward enhancing RALM reliability.