iai_ MSU at S em E val-2025 Task-3: Mu- SHROOM , the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes in E nglish (2025)

Abstract: This paper presents the submissions of the iai_MSU team for SemEval-2025 Task 3 – Mu-SHROOM, where we achieved first place in the English language. The task involves detecting hallucinations in model-generated text, which requires systems to verify claims against reliable sources.In this paper, we present our approach to hallucination detection, which employs a three-stage system. The first stage uses a retrieval-based (Lewis et al., 2021) to verify claims against external knowledge sources. The second stage applies the Self-Refine Prompting (Madaan et al., 2023) to improve detection accuracy by analyzing potential errors of the first stage. The third stage combines predictions from the first and second stages into an ensemble.Our system achieves state-of-the-art performance on the competition dataset, demonstrating the effectiveness of combining retrieval-augmented verification with Self-Refine Prompting. The code for the solutions is available on https://github.com/pansershrek/IAI_MSU.

## introduction large language models (llms) have revolutionized natural language processing (nlp), demonstrating strong capabilities in text generation, summarization, and dialogue systems. however, llms remain prone to hallucinations, where generated content contains false, misleading, or unverifiable information. addressing this issue is crucial for real-world applications, especially in domains requiring high factual accuracy, such as journalism, medicine, and law. the ability to detect and mitigate hallucinations is essential to improve the reliability and trustworthiness of llm-generated content. semeval-2025 task 3 -mu-shroom foot_1 @xcite introduces a multilingual hallucination detection challenge, requiring participants to identify specific spans of hallucinated text within model-generated output. unlike traditional factchecking tasks, mu-shroom provides llmgenerated text alongside tokenized representations and logit scores, and participants must compute a probability score for each character, indicating its likelihood of being a hallucination. the task covers 14 languages, including english, chinese, arabic and several european languages, presenting unique challenges such as linguistic diversity, cross-lingual hallucination patterns, and variations in model behavior. to tackle these challenges on english language, we propose a three-stage hallucination detection system: stage 1: we employ a retrieval-augmented generation (rag) pipeline, using wikipedia as an external knowledge source to verify input claims. stage 2: we employ an self-refine prompting strategy, where an llm re-evaluates the first-stage output to identify potential errors and refine hallucination predictions. stage 3: we use an ensemble strategy that merges three predictions from the first stage and three from the second stage to create the final submission. ## related works hallucination detection in large language models (llms) is a critical area of research, focusing on identifying and mitigating instances where models generate content that is plausible but factually incorrect. various approaches have been proposed to address this challenge, including methods utilizing llms themselves, retrieval-augmented verification techniques, and self-refinement prompting strategies. llms can be utilized to detect hallucinations by analyzing their internal states and output. in "unsupervised real-time hallucination detection based on the internal states of large language models," @xcite the authors propose mind, an unsupervised training framework that leverages the internal states of llms for real-time hallucination detection without requiring manual annotations. this approach utilizes the model's internal representations during inference to identify incoherent or factually inaccurate responses. but we face a more difficult task as soon as we have only tokens' logits. another study, "hallucination detection: robustly discerning reliable answers in large language models" @xcite introduces a robust discriminator named reld to effectively detect hallucinations in llm-generated answers. reld is trained on a bilingual question-answering dialogue dataset, enabling it to identify unfaithful or inconsistent content generated by diverse llms. integrating external knowledge sources into the generation process can enhance the factual accuracy of llm output. in "mitigating hallucinations in large language models via self-refinement-enhanced knowledge graph retrieval" @xcite the authors propose re-kgr, a method that augments the factuality of llms' responses by leveraging knowledge graph retrieval. this approach identifies tokens with a high potential for hallucination and refines the associated knowledge triples to reduce verification efforts. similarly, "self-alignment for factuality: mitigating hallucinations in llms via self-alignment" @xcite introduces sk-tuning, a strategy that improves an llm's confidence estimation and calibration, thereby enhancing its self-evaluation ability. this method aligns the model's output with external knowledge to mitigate hallucinations. self-refinement prompting strategies involve iterative processes where llms generate, evaluate, and refine their output to improve factual accuracy. the "self-refine" @xcite approach allows llms to iteratively refine output and incorporate feedback along multiple dimensions to improve performance on diverse tasks. this method does not require supervised training data or reinforcement learning and works with a single llm. additionally, "towards mitigating hallucination in large language models via self-reflection" @xcite proposes an innovative self-reflection method to mitigate hallucination in llms. the iterative feedback loop process generates, scores, and refines responses to reduce hallucinations, particularly in medical question-answering systems. ## conclusion in this paper, we presented the submissions of the iai_msu team for semeval-2025 task 3 -mu-shroom, where we achieved first place in the english language track. our approach combines retrieval-augmented generation (rag) with self-refine prompting, demonstrating the effectiveness of integrating external knowledge verification with iterative model refinement. we introduced a threestage pipeline where the first stage uses a ragbased method to verify claims, followed by the second stage where self-refine prompting refines hallucination detection output for improved accuracy. additionally, the use of ensemble techniques further enhanced robustness by aggregating output from multiple runs and varying temperature settings. our final submission achieves 65.09% iou and 62.94% corr, confirming the strength of our methodology in detecting hallucinations and improving the factual accuracy of model-generated text.