YNU - HPCC at S em E val-2025 Task3: Leveraging Zero-Shot Learning for Halluciantion Detection (2025)

Abstract: This study reports the YNU-HPCC team’s participation in SemEval-2025 shared task 3, which focuses on detecting hallucination spans in multilingual instruction-tuned LLM outputs. This task differs from typical hallucination detection tasks in that it does not require identifying the entire response or pinpointing which sentences contain hallucinations generated by the LLM. Instead, the task focuses on detecting hallucinations at the character level. In addition, this task differs from typical hallucination detection based on binary classification. It requires not only identifying hallucinations but also assigning a likelihood score to indicate how likely each part of the model output is hallucinatory. Our approach combines Retrieval-Augmented Generation (RAG) and zero-shot methods, guiding LLMs to detect and extract hallucination spans using external knowledge. The proposed system achieved first place in Chinese and fifteenth place in English for track3.

## introduction hallucination in large language models refers generating of information that appears plausible but is factually incorrect or fabricated. this issue is common in open-domain tasks, such as question answering and summarization, where the model may produce answers inconsistents with the provided context or external knowledge. hallucinations can be categorized into two main types @xcite : intrinsic, which conflicts with the source content, and extrinsic, which cannot be verified from the source content. these errors are closely related to the nature of knowledge. some knowledge is static, such as the date of the civil war, while other knowledge evolves 1 our code is available at https://github.com/ deepdarklowtech/ynu-hpcc-semeval2025-task3 over time, such as the current population of china. the distinction between these two types of knowledge implies that hallucinations cannot be eliminated-especially for the latter-unless the model adopts a retrieval-augmented generation (rag)based @xcite approach. as a result, much effort has been dedicated to hallucination detection. these detection methods can be broadly classified into the following categories @xcite : 1) methods based on model output logits, such as uncertainty and semantic entropy; 2) fact-based detection methods, which generally calculate the similarity between factual documents and the model's output. the shortcomings of both approaches are evident: the former can only detect hallucinations but cannot correct them, and it requires the detection process to be tightly coupled with model generation. the latter, on the other hand, struggles in domains where factual documents are difficult to retrieve. this task evaluated whether the model's output addressed the question and whether the answer was accurate. neither of these approaches effectively addresses the task's minimum interval requirement because both focus on the entire model output rather than specific spans within the answer. therefore, our team's approach initially combined fact-based documentation with machine reading comprehension (mrc) (kenton and toutanova, 2019), followed by integrating the factual document-based method with zero-shot detection. the final experimental results demonstrated that our solution was both effective and competitive @xcite . ## experiment detail datasets. in our mrc solution, we fine-tuned the llama model using ragtruth and halueval datasets. ragtruth explicitly labels hallucination intervals in numerical form, closely aligning with the task requirements, while halueval only identifies the text segments containing hallucinations without specifying precise intervals. evaluation metric. this task adopts iou and cor as evaluation metrics during the assessment phase. the scoring criteria for iou are outlined below: cor's score was calculated using the spearman rank correlation coefficient, which is calculated as follows: ## result in the final evaluation phase, the tournament organizers used intersection-over-union (iou) to evaluate the hard_labels, while spearman correlation was used to assess the soft_labels. the mrc-based approach using llama for hallucination interval detection also performs well. to better demonstrate the effectiveness of our team's solution, we also performed a more refined data analysis at the end of the evaluation phase. our analysis is based on the labeled test set released by the organizers at the end of the evaluation phase. as we stated in section 3.2, the granularity of the labels provided by the organizers is lower than our ## analysis the data in the table indicates a low correlation in our solution, which can be attributed to at least the following factors: ## conclusion this study describes the work conducted by the ynu-hpcc team for participation in "mu-shroom (semeval 2025)." the methods we employed include augmented document-based mrc and augmented document-based prompt engineering. the final results showed that using openai-o1 with augmented document-based prompt engineering achieved first place in the chinese track, with an iou score of 0.5540 and a cor score of 0.3518. in the english track, the model achieved 15th place, with an iou score of 0.4807 and a cor score of 0.4075. future work attempts to adopt a knowledge graph-based approach for self-checking llmgenerated answers in the future. after all, compared to a simplistic model, a more intelligent model that outputs incorrect answers tends to cause more significant harm to society and the economy. therefore, integrating online retrieval-augmented generation (rag) with offline knowledge graphs will be key for mitigating hallucination.