Hallucination Detectives at S em E val-2025 Task 3: Span-Level Hallucination Detection for LLM -Generated Answers (2025)

Abstract: Detecting spans of hallucination in LLM-generated answers is crucial for improving factual consistency. This paper presents a span-level hallucination detection framework for the SemEval-2025 Shared Task, focusing on English and Arabic texts. our approach integrates Semantic Role Labeling (SRL) to decompose the answer into atomic roles, which are then compared with a retrieved reference context obtained via question-based LLM prompting. Using a DeBERTa-based textual entailment model, we evaluate each role’s semantic alignment with the retrieved context. The entailment scores are further refined through token-level confidence measures derived from output logits, and the combined scores are used to detect hallucinated spans. Experiments on the Mu-SHROOM dataset demonstrate competitive performance. Additionally, hallucinated spans have been verified through fact-checking by prompting GPT-4 and LLaMA. Our findings contribute to improving hallucination detection in LLM-generated responses.

## introduction llms have demonstrated remarkable capabilities in generating human-like text, enabling a wide range of applications, including questionanswering, summarization, and conversational agents. however, these models often produce hallucinations that appear plausible but are factually incorrect or unsupported by the input context @xcite . current research efforts focus on different approaches to detect and mitigate hallucinations. some methods rely on sentence-level classification, where the entire response is labeled factual or not @xcite . while these techniques provide a general assessment, they lack the granularity to pinpoint the specific hallucinated segments, leading to challenges in localized error correction @xcite . other works explore token-level approaches, which utilize features such as minimum and average token probabilities to identify hallucinated content @xcite . this strategy shows promising results, but may struggle to effectively combine probabilistic and semantic information. these issues become more pronounced in morphologically rich and linguistically diverse languages such as arabic, where complex syntactic rules and dialectal variations add layers of difficulty @xcite . recent research highlights the need for improved hallucination detection techniques tailored to arabic and other low-resource languages @xcite . our research introduces a span-level hallucination detection framework to address these challenges. unlike previous sentence-level or tokenlevel approaches, our framework identifies hallucinated spans by decomposing llm-generated answers into semantic units using semantic role labeling (srl) and dependency parsing. these units are evaluated against context retrieved using gpt-4, then contradiction detection using pretrained textual entailment bert model. simultaneously, token-level confidence scores are computed to capture the model's certainty for each unit. the combined scores provide a refined measure of factuality which indicates hallucination parts. we evaluate our framework for both english and arabic languages using the multilingual mu-shroom dataset as part of the semeval-2025 shared task. to further strengthen reliability, we incorporate an llm-based verification step by using fact-checking technique using 2 different llms (gpt-4, and llama). the remainder of this paper is organized as follows: section 2 reviews related work. section 3 introduces the dataset's structure. section 4 presents our methodology. section 5 outlines the experimental results. finally, section 6 concludes with key findings and future work. ## related work hallucination detection in llms has been studied, with research mainly focusing on sentencelevel classification. these approaches determine whether an entire response is factual or hallucinated, but lack the granularity to identify specific hallucinated spans. token-based methods leverage confidence measures to estimate factuality, but often fail to account for semantic inconsistencies within generated responses @xcite . recent advances have emphasized the need for hallucination detection at the spectral level, allowing a finer-grained factual assessment @xcite . reference-based methods compare generated text against external sources such as factcheck-bench @xcite , a fine-grained factchecking benchmark, and halogen @xcite , a large-scale hallucination evaluation suite that categorizes hallucination errors. other techniques, such as interrogatellm @xcite , employ self-consistency verification, where llms are prompted multiple times to detect contradictions in their responses. although these methods improve factual verification, they operate primarily at the response level rather than identifying hallucinated spans. textual entailment models have also been explored for hallucination detection, classifying responses into entailment, contradiction, or neutrality @xcite . however, these approaches are typically sentence-level, limiting their effectiveness for pinpointing hallucinated spans @xcite . the detection of low resources languages hallucinations presents additional challenges, particularly in morphologically rich languages like arabic, where syntactic complexity and dialectal variations complicate the factual verification @xcite . datasets such as halwasa @xcite and acqad @xcite were developed to help in analyzing factual and linguistic inaccuracies. research on hallucination detection in arabic has largely focused on sentence-level classification, leaving a gap in span-level hallucination identification @xcite . some studies have introduced dependency parsing techniques and semantic role labeling (srl) to improve hallucination detection which highlight the role of syntactic decomposition in improving the evaluation of facts @xcite . advances in srl for information extraction should also contributed to hallucination detection, particularly in low-resource language. unlike traditional information extraction tasks, srl-based techniques focus on verifying atomic claims within generated text, enhancing factual alignment with external sources. although recent studies focus on hallucination detection, there remains a need for more linguistically adaptive approaches to improve the factual consistency in llm-generated text. ## dataset structure the dataset used in this research is provided by the semeval-2025 shared task, mu-shroom (multilingual shared-task on hallucinations and related observable over generation mistakes). this dataset contains examples in multiple languages, including english, arabic, spanish, and french. each example in the dataset comprises a questionanswer pair, the corresponding llm-generated output, and annotations for hallucinated spans. ## span-level hallucination detection methodology our framework detects hallucinated spans in llmgenerated answers by combining semantic analysis with token-level probabilistic measures, as described in figure 1 . the system comprises several components: context retrieval for the question, answer decomposition, confidence scoring, textual entailment, score integration to get refined score which indicates the hallucination parts. the framework is designed to handle both english and arabic, utilizing tools and models suitable for each language. ## expriments and results analysis our experiments focus on two languages (english and arabic) and assess performance using both intrinsic evaluation metrics and fact-checking verification with llms. ## conclusion this paper presents a span-level hallucination detection framework that integrates semantic role labeling (srl), textual entailment, and token-level confidence scoring to identify hallucinations in llm-generated answers. evaluated on the mu-shroom dataset, our approach achieves an iou of 0.358 and a cor of 0.322 in english, and an iou of 0.28 and a cor of 2.1 in arabic when using camelparser combined with the srl extraction algorithm. our results emphasize the effectiveness of dependency parsing before srl extraction, particularly in arabic, where linguistic complexity poses additional challenges. despite these improvements, challenges remain for morphologically rich languages. future work will focus on enhancing entailment models and addressing additional syntactic structures, such as nominal sentences in arabic and long complex sentences.