When Do Multimodal and Graph-Augmented RAG Help? A Controlled Evaluation for Document Question Answering

Evaluating RAG approaches for Question Answering

Cross-Lingual Indirect Prompt Injection Across Retrieval, Reranking, And Generation In Multilingual RAG

External evidence can make retrieval-augmented generation (RAG) more informative, yet retrieved passages also provide a path for adversarial instructions to enter the model context. We examine that path in an English-Indonesian RAG system and track cross-lingual indirect prompt injection separately at retrieval, reranking, and generation. The experiment starts from 75 semantic items and evaluates every item in eight query/body/payload language combinations, for 600 paired trials. Qwen3-Embedding-0.6B and BGE-M3 produce top-20 candidate sets, BGE-reranker-v2-m3 reduces each set to five documents, and Qwen3-0.6B answers from the resulting context with either a standard prompt or an explicit trust-boundary prompt. Statistical uncertainty is estimated by resampling semantic items, and paired binary outcomes are modeled with generalized estimating equations. Poison documents reached the top 20 in 80.50% of Qwen trials and 37.67% of BGE-M3 trials (odds ratio 6.94, 95% CI 4.53-10.63). Top-five exposure was 18.50% and 16.17%, respectively. Standard end-to-end attack success was 6.33% for Qwen and 6.00% for BGE-M3; boundary-aware prompting lowered both rates to 1.33%, with no canary false positives. The results indicate that multilingual RAG security depends on several linked stages rather than generation alone.

RA-QGQA: A Question-Driven Pipeline for Corpus-Grounded Knowledge Graph Verification

RA-QGQA is presented, which recasts triple verification as a question-driven, corpus-grounded task, and demonstrates RA-QGQA as an interactive web system in which users import a KG and its source corpus, verify all triples in a single pass, and inspect the passages that justify its verdict.

Retrieval Granularity as Evidence Design in Small-Model RAG Question Answering: A Diagnostic HotpotQA Study

Results align with a diagnostic perspective on chunking: using evidence at a task-appropriate level of granularity can improve grounding, auditability, and answer quality, but the observed patterns should be interpreted within the HotpotQA distractor setting, fixed generator, and tested context budgets.

FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering

Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transformed many areas, their application in fashion remains limited by hallucinations and weak domain specialization. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) offers a promising way to add structured knowledge to LLMs. However, existing fashion KGs are typically restricted to product-level attributes or item relations, and fail to capture the broader fashion ecosystem. To bridge these gaps, we propose \textbf{FashionEcoKG}, a comprehensive, domain-wide knowledge graph built with expert-level precision and professionalism. It is constructed through a three-stage agentic pipeline that extracts high-fidelity knowledge cores from authoritative textbooks and strengthens structural connectivity through cross-domain augmentation and generative expansion. To leverage this resource, we further develop \textbf{PG-RAG} (Pruning-Grounding RAG), a training-free framework designed to handle the conceptual density and linguistic noise of fashion queries. Specifically, we introduce a Dual-Granularity Path Re-Ranking (DGPR) module of two stages. The Pruning-based Semantic Ranking (PSR) module distills each query into a skeleton form to improve retrieval recall, while the Grounding-based Agentic Ranking (GAR) performs point-wise scrutiny of candidate paths against the original full query to ensure global relevance. Experiments on a curated fashion QA dataset show that PG-RAG effectively leverages FashionEcoKG to improve retrieval and answer accuracy, outperforming both non-RAG and existing KG-RAG baselines.

MissDiag: Diagnostic Evaluation of Incomplete-Knowledge Robustness in KGQA and KG-RAG

Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, and incomplete. Existing robustness evaluations usually report aggregate changes in answer quality after evidence is removed or perturbed, which measures sensitivity to incomplete support but leaves the source of degradation under-specified: the same score change can conflate the type of missing evidence, the response of the evaluated system, and the sensitivity of the answer-matching protocol. To address this gap, we propose \textbf{MissDiag}, a diagnostic evaluation framework for incomplete-knowledge robustness in KGQA and KG-RAG. MissDiag keeps the question and gold answer fixed while applying structurally typed missingness interventions to benchmark-provided support graphs, enabling paired comparisons that decompose robustness changes by evidence type, system response, and evaluation protocol rather than reducing them to a single aggregate score drop. Experiments across multiple system families show that incomplete-knowledge robustness is better understood as a typed degradation phenomenon than as a uniform property: answer-adjacent evidence loss produces the largest observed degradation, source-context removal is often neutral and can be beneficial, and semantic answer matching changes absolute scores while preserving the main typed degradation patterns. By transforming aggregate robustness measurement into typed diagnostic attribution, MissDiag provides a more interpretable basis for comparing, diagnosing, and stress-testing KGQA and KG-RAG systems under incomplete knowledge.