H allu S earch at S em E val-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection (2025)

Abstract: We present HalluSearch, a multilingual pipeline designed to detect fabricated text spans in Large Language Model (LLM) outputs as part of Mu-SHROOM. HalluSearch couples retrieval-augmented verification with fine-grained factual splitting to identify and localize hallucinations in 14 different languages. Empirical evaluations show that HalluSearch performs competitively, placing fourth in both English (within the top 10%) and Czech. While the system’s retrieval-based strategy generally proves robust, it faces challenges in languages with limited online coverage, underscoring the need for further research to ensure consistent hallucination detection across diverse linguistic contexts.

## introduction ever since the introduction of the transformer architecture @xcite and more specifically with the rise of decoder-only large language models (llms), significant advances in the field of natural language processing have been made llms excel at text generation and are widely used for tasks such as translation, summarization, and question answering. despite their impressive capabilities, being statistical language models, llms can sometimes produce factually incorrect or inaccurate statements, often presented in a very convincing manner. this phenomenon is commonly referred to as hallucination. hallucination is a significant drawback of llms which vary in scale from a few billion to hundreds of billions of parameters @xcite . it has been observed that relatively smaller models tend to hallucinate more frequently due to limitations in their training data and model complexity @xcite . hallucinations in llms can have grave consequences. for example, in critical domains like healthcare, relying on an llm that hallucinates, for diagnosis, can lead to deaths or disabilities. similarly, in the business and technology sectors, hallucinations may result in poor decision-making, leading to significant financial losses and misallocated investments. therefore, detecting and localizing hallucinated segments in llm responses is crucial for developing trustworthy llm-driven applications. mu-shroom, is a multilingual shared-task on hallucinations and related observable overgeneration mistakes @xcite and is and extension to an earlier task, shroom @xcite and is part of semeval-2025. the task aims to identify spans of text corresponding to hallucinations within a multilingual context, covering 14 languages: arabic (modern standard), basque, catalan, chinese (mandarin), czech, english, farsi, finnish, french, german, hindi, italian, spanish, and swedish. in this shared task, each datapoint consists of an output string generated by one of the publicly available llms in response to a user query. the goal is to calculate, on a character-level, the probability that a character is part of a hallucination span. in this work, we propose a novel system, hallusearch, designed to detect hallucinated spans in multilingual llm responses. our approach utilizes retrieval-augmented generation (rag) as introduced by @xcite where external, trusted sources are used to factcheck a model's response which in turn helps determine which parts of a model's response are fabricated, or factual. our experiments show that hallusearch achieves competitive performance in several languages such as, english and czech. ## related work as stated earlier, the term 'hallucination' has been recently used to describe a challenging phenomenon in the context of llms. this phenomenon involves generated content that is nonsensical or unfaithful to the input or context provided. the work of @xcite reexamines the notion of hallucinations in llms through the lens of human psychology. the authors argue that traditional use of this term may be misleading when applied to ai-generated content and emphasize the value of a psychologically informed approach depending on cognitive dissonance, suggestibility, and confabulation as the basis for their approach to mitigate hallucinations and other issues in llms. @xcite formally addressed hallucination for llms by employing results from learning theory findings and demonstrated that hallucination is inevitable for all computable llms. halueval proposed by @xcite provided a large-scale benchmark for evaluating an llm's ability to recognize hallucinations. tonmoy et al. @xcite surveyed various strategies for mitigating hallucinations, offering insights into potential solutions for this persistent issue. shroom was launched to address the challenge of detecting hallucinations in output generated by natural language generation (nlg) systems. solutions such as halu-nlp @xcite and opdai (chen et al., 2024) have been proposed to tackle these challenges. however, pinpointing the exact locations of hallucinations was not addressed by this task. mu-shroom, the current iteration of shroom, has thus introduced this goal while narrowing the focus to question answering and corresponding llms outputs. ## methodology and experimental setup our hallusearch pipeline includes three major parts: (1) factual splitting, (2) context retrieval, and (3) hallucination verification. the pipeline begins with an input of user query and an llmgenerated response. it then employs the factual splitting module to divide the text into discrete factual statements. next, the context retrieval stage uses search results from wikipedia to gather relevant background information for each query. finally, the hallucination verification step compares each statement to its retrieved context counterpart and annotates any spans that looks unsupported or incorrect at a character-level granularity, resulting in an output of labeled hallucinated segments, as shown in figure 1 . ## results in our experiments ( conversely, attempts to combine multiple opensource models and voting ensemble did not enhance results, possibly due to inconsistent alignment between the models' annotations. overall, hallusearch's approach to fact verification demonstrates competitiveness across diverse languages, but the variation in rank shows the complexities that remain an open challenge in multilingual hallucination span detection. ## conclusion in this work, we have presented hallusearch, the aim of which was to address the problem of detecting llm hallucinations. hallusearch is a search-enhanced rag pipeline that pinpoints potentially fabricated or incorrect spans in multilingual outputs. by using precise factual splitting, context retrieval from reliable sources, and a prompt-based verification step, our system provides both hard-label and soft-label annotations for hallucination spans. evaluation results demonstrate that hallusearch competes well in multilingual settings despite inherent difficulties such as limited content availability in lessresourced languages. these findings highlight the importance of robust, cross-lingual retrieval strategies and careful prompt engineering. future research will delve into addressing low-resource languages more effectively, improving fallback mechanisms, and exploring fine-grained alignment techniques for enhanced span detection accuracy.