Multilingual Retrieval-Augmented Generation (mRAG) systems enable language models to answer knowledge-intensive queries with citation-supported responses across languages. While such systems have been proposed, an open questions is whether the mixture of different document languages impacts generation and citation in unintended ways. To investigate, we introduce a controlled methodology using model internals to measure language preference while holding other factors such as document relevance constant. Across eight languages and six open-weight models, we find that models preferentially cite English sources when queries are in English, with this bias amplified for lower-resource languages and for documents positioned mid-context. Crucially, we find that models sometimes trade-off document relevance for language preference, indicating that citation choices are not always driven by informativeness alone. Our findings shed light on how language models leverage multilingual context and influence citation behavior. Retrieval-Augmented Generation (RAG) systems have become a core component of modern large language model (LLM) pipelines, enabling models to answer knowledge-intensive queries by supplementing their limited parametric knowledge with external information (Lewis et al., 2020; Karpukhin et al., 2020; Gao et al., 2024). Given that over 50% of digital content is produced in languages other than English (Statista, 2025), recent work has extended these systems to multilingual RAG (mRAG) settings, which handle queries and documents in languages beyond English (Chirkova et al., 2024; Wu et al., 2024). Despite recent advances, prior work highlights a key challenge in mRAG systems: language preference - a systematic tendency of models to favor sources written in certain languages during generation (Park & Lee, 2025). Understanding this behavior is crucial, as citation patterns shape both the information users see and the languages prioritized in multilingual knowledge access. Existing approaches to measuring language preference, however, often fail to capture citation correctness. In short-form mRAG, preference has been estimated via information overlap (Sharma et al., 2025) or embedding similarity (Park & Lee, 2025), which do not directly account for correctness. In long-form mRAG, where outputs contain in-line citations (Zheng et al., 2025; Xu & Peng, 2025), preference has typically been measured by comparing citation frequencies against the language distribution of retrieved documents.

Reliably determining the performance of Retrieval-Augmented Generation (RAG) systems depends on comprehensive test questions. While a proliferation of evaluation frameworks for LLM-powered applications exists, current practices lack a systematic method to ensure these test sets adequately cover the underlying knowledge base, leaving developers with significant blind spots. To address this, we present a novel, applied methodology to quantify the semantic coverage of RAG test questions against their underlying documents. Our approach leverages existing technologies, including vector embeddings and clustering algorithms, to create a practical framework for validating test comprehensiveness. Our methodology embeds document chunks and test questions into a unified vector space, enabling the calculation of multiple coverage metrics: basic proximity, content-weighted coverage, and multi-topic question coverage. Furthermore, we incorporate outlier detection to filter irrelevant questions, allowing for the refinement of test sets. Experimental evidence from two distinct use cases demonstrates that our framework effectively quantifies test coverage, identifies specific content areas with inadequate representation, and provides concrete recommendations for generating new, high-value test questions. This work provides RAG developers with essential tools to build more robust test suites, thereby improving system reliability and extending to applications such as identifying misaligned documents.

Retrieval-Augmented Generation (RAG) is a pivotal technique for enhancing the capability of large language models (LLMs) and has demonstrated promising efficacy across a diverse spectrum of tasks. While LLM-driven RAG systems show superior performance, they face unique challenges in stability and reliability. Their complexity hinders developers' efforts to design, maintain, and optimize effective RAG systems. Therefore, it is crucial to understand how RAG's performance is impacted by its design. In this work, we conduct an early exploratory study toward a better understanding of the mechanism of RAG systems, covering three code datasets, three QA datasets, and two LLMs. We focus on four design factors: retrieval document type, retrieval recall, document selection, and prompt techniques. Our study uncovers how each factor impacts system correctness and confidence, providing valuable insights for developing an accurate and reliable RAG system. Based on these findings, we present nine actionable guidelines for detecting defects and optimizing the performance of RAG systems. We hope our early exploration can inspire further advancements in engineering, improving and maintaining LLM-driven intelligent software systems for greater efficiency and reliability.

Retrieval-Augmented Generation (RAG) systems represent a significant advancement over traditional Large Language Models (LLMs). RAG systems enhance their generation ability by incorporating external data retrieved through an Information Retrieval (IR) phase, overcoming the limitations of standard LLMs, which are restricted to their pre-trained knowledge and limited context window. Most research in this area has predominantly concentrated on the generative aspect of LLMs within RAG systems. Our study fills this gap by thoroughly and critically analyzing the influence of IR components on RAG systems. This paper analyzes which characteristics a retriever should possess for an effective RAG's prompt formulation, focusing on the type of documents that should be retrieved. We evaluate various elements, such as the relevance of the documents to the prompt, their position, and the number included in the context. Our findings reveal, among other insights, that including irrelevant documents can unexpectedly enhance performance by more than 30% in accuracy, contradicting our initial assumption of diminished quality. These results underscore the need for developing specialized strategies to integrate retrieval with language generation models, thereby laying the groundwork for future research in this field.

Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. This study aims at the feasibility of a zero-shot reader that addresses the challenges of computational cost and the need for labeled data. We find that LLMs are distracted due to irrelevant documents in the retrieved set and the overconfidence of the generated answers when they are exploited as zero-shot readers. To tackle these problems, we mitigate the impact of such documents via Distraction-aware Answer Selection (DAS) with a negation-based instruction and score adjustment for proper answer selection. Experimental results show that our approach successfully handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. Furthermore, unlike supervised readers struggling with unseen data, zero-shot readers demonstrate outstanding transferability without any training.

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.