Benchmarking retrieval augmented generation LLMs for Arabic noise robustness

Visual Token Compression Enhances Robustness of MLLMs

In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vulnerabilities such as jailbreak attacks and hallucinations. Given that vision and language modalities cannot be perfectly aligned, the misaligned visual tokens might act as out-of-distribution (OOD) inputs, leading to unpredictable outputs and introducing potential vulnerabilities. Building on this insight, we aim to enhance model robustness against jailbreaks and hallucinations by reducing OOD visual tokens at robust-pruning layers, while also reducing inference cost as a side benefit. Specifically, we measure the distance between each visual token and the language feature space. Then, visual tokens with large distances are identified as OOD tokens, which can be iteratively pruned. To demonstrate the effectiveness of our method, we evaluate it on seven diverse popular benchmarks. Notably, our method yields an average improvement of 13.29\% in defending jailbreak attacks, consistently achieves competitive performance in mitigating hallucinations, and maintains strong results on general datasets like MME.

Benchmarking and AI-assisted human-like evaluation of retrieval-augmented generation for Arabic and English documents

Large Language Models Hallucinate and How Retrieval- Augmented Generation Mitigates It

Large Language Models (LLMs) can generate fluent and convincing responses, but fluency does not guarantee factual correctness. Hallucination occurs when a model produces information that is false, unsupported, or inconsistent with available evidence. This paper reviews why hallucinations arise andexamine Retrieval-Augmented Generation (RAG) as a practical mitigation strategy. The discussion focuses on prediction-based generation, missing or outdated knowledge, ambiguous queries, and the absence of automatic verification. RAG addresses these limitations by retrieving relevant information from an external knowledge source and supplying it to the generator as contextual evidence. The paper describes the major stages of a RAG pipeline, including document ingestion, chunking, embeddings, vector storage, retrieval, context augmentation, and answer generation. It also examines the conditions under which RAG can fail, including poor retrieval, incomplete knowledge bases, unreliable sources, weak ranking, and incorrect interpretation of retrieved context. Finally, the paper discusses evaluation dimensions such as context relevance, answer faithfulness, and answer relevance, and outlines applications of RAG in education, enterprise search, technical support, research assistance, and document question answering. The analysis concludes that RAG should be viewed as a grounding and evidence-access mechanism rather than a guarantee of hallucination-free generation

RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety

Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has demonstrated that retrieval-augmented generation (RAG) can have unintended side effects on the overall safety of the generated responses, when prompted for harmful or dangerous content. A clearer understanding of the mechanisms leading to this result is needed, as increasing numbers of end users turn to RAG to incorporate corporate documents and knowledge bases into LLM-based systems. We introduce RAG-Safety-Bench, a benchmark to measure the safety impact of RAG on LLM models. By removing the confounding effect of retriever quality, and cleanly separating the problem into four conditions -- non-RAG, RAG with an oracle document containing the answer to the harmful request, RAG with documents related to the harmful request but without the specific answer, and RAG with random, safe documents -- the benchmark isolates the impacts of different factors in the observed safety degradation. We report results across five open-source LLMs, showing an inverse relationship between benign and unsafe capability, strong evidence that baseline safety guardrails do not lead to downstream safety guarantees in the RAG case, and model-specific support for previous findings that even benign documents can lead to unsafe generation in retrieval-enabled systems.

Numerical Hallucinations in Retrieval-Augmented Generation: Detection and Analysis

These findings highlight numerical fabrication as a critical gap in current hallucination detection approaches and recommend the need for specialized, number-aware methods in RAG systems.

An Optimization Framework for Retrieval Augmented Generation in Indonesian Educational Question Answering

A RAG optimization framework for Indonesian-language educational question answering using a Human-Computer Interaction learning corpus as a case study is developed and provides a procedure for selecting retrieval and generation settings for a given corpus.