Team Cantharellus at S em E val-2025 Task 3: Hallucination Span Detection with Fine Tuning on Weakly Supervised Synthetic Data (2025)

Abstract: This paper describes our submission to SemEval-2025 Task-3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, which mainly aims at detecting spans of LLM-generated text corresponding to hallucinations in multilingual and multi-model context. We explored an approach of fine-tuning pretrained language models available on Hugging Face. The results show that predictions made by a pretrained model fine-tuned on synthetic data achieve a relatively high degree of alignment with human-generated labels. We participated in 13 out of 14 available languages and reached an average ranking of 10th out of 41 participating teams, with our highest ranking reaching the top 5 place.

## introduction recent years have witnessed the rapid development of large language models (llms) and their applications in various fields of natural language processing @xcite . however, content generated by llms occasionally contains inaccurate or fictitious information @xcite . the phenomenon in which natural language generation models often generate text that is nonsensical, or unfaithful to the provided source input is commonly referred to as "hallucinations" @xcite . it is therefore a vital task to detect and identify hallucinated content so as to improve the reliability and trustworthiness of llm-generated content. semeval 2025 task 3 (mu-shroom, the multilingual shared-task on hallucinations and related observable overgeneration mistakes) @xcite proposes the task of detecting hallucination in content generated with llms. different from the previous iteration, semeval-2024 task 6 @xcite , in which the participants * equal contribution, authors are listed alphabetically. as is shown in the results of the previous iteration of this task @xcite , it is effective to fine-tune pretrained language model for hallucination detection. we therefore further extended this approach from performing binary classification tasks to predict the spans of hallucinations. we explored fine-tuning a series of transformerbased pretrained language models @xcite , including text-to-text transformer models @xcite and bert-based models @xcite , with training data created by ourselves. building on this method, we developed the system based and participated in 13 out of 14 languages. in addition, we also applied the approach of named entity recognition (ner) as a baseline in order to provide a more comprehensive evaluation of our system's performance. we have released our code and other relevant material on github foot_0 . ## background hallucination detection has been an extensively researched topic in recent years. one promising solution for hallucination detection is to utilize the self-evaluation ability of llms to judge the factual correctness of a given statement, leveraging the fact that llms have possessed a rich knowledge base @xcite . many existing studies focus on the binary classification of hallucination. for instance, selfcheckgpt @xcite is built on the idea that when an llm is familiar with a particular concept, the responses it generates are likely to be consistent and con-tain similar facts. in the halueval 2.0 benchmark @xcite , the hallucination detection approach is built by first extracting factual statements from llm responses, then determining the trustfulness of these statements with respect to world knowledge by taking advantage of the vast knowledge base of llms. similarly, mind @xcite introduces a similar approach that leverages the internal states of llms for real-time hallucination detection without requiring manual annotations. grapheval @xcite proposes a method of detecting inconsistencies with respect to provided knowledge using a knowledge-graph approach. prompt engineering is a crucial technique for extending the capabilities of llms @xcite . a commonly used strategy is few-shot prompting, which refers to the technique of constructing prompts using a small set of demonstrative input-output examples @xcite . another method that has been proved effective is chain-of-thought (cot) prompting, which is achieved by guiding the llm to think step by step to perform complex reasoning tasks @xcite @xcite . named entity recognition (ner) (yadav and bethard, 2019) is the task of identifying named entities such as person, location and organization in text, which are often central to factual inconsistencies in llm-generated text. deep learning approaches have increasingly been adopted for ner and have exhibited impressive abilities across various domains and languages @xcite @xcite . recent research also explores the possibility of integrating prompting techniques into ner tasks by utilizing llms @xcite . while ner is limited to identifying predefined entity types and cannot assess the broader context or relationships between entities, it can still serve as a tool for detecting potential sources of hallucinations, making it a possible referential benchmark. ## results our submission included 21 models in total, with different combinations of models and training data used. these were the following: as opposed to the initial submission, when the models were trained on the generated answers only (model_output_text), we have conducted an additional test, and trained xlm-roberta-large base model again with the same parameters, but using a concatenation of the questions and the answers as the training input, separating them with an additional special token '<@@>'. we have used similar joint input for inference and adjusted the spans accordingly. the results of such training have outperformed all of our other approaches, with a significant increase of scores for all of the languages except english and chinese. a comparative summary of the models' iou scores is shown in appendix a. ## conclusion and limitations as our participation in semeval-2025 task-3, we proposed the approach of fine-tuning language models with synthetic data for hallucination span prediction. the results demonstrate that our system achieved competitive scores across various languages. our approach is proved effective as the scores rank as high as 5th in certain languages. the fact that the model is exclusively fine-tuned on data constructed by llm suggests that this approach is a feasible and effective strategy for the task of hallucination span prediction, particularly under the circumstance where human-labeled training data is absent. however, it is worth pointing out that llm-based synthetic data are potentially more heavily subject to limitations compared with hand-crafted data. a closer examination suggests that the quality of the synthetic data does not match that of the validation and test sets provided by the organizers. across various languages, the synthetic data can often be shorter or significantly longer in output length, cover a narrower range of topics, and propose less sophisticated question-andanswer pairs than those in the validation and test sets. these problems can supposedly be addressed through few-shot prompting and more elaborate prompt-engineering. it is also important to mention, that although various efforts have been made in both prompting and post-processing to increase the likelihood that the spans correctly indicate the hallucinated texts, the final output is still prone to errors and may not match the accuracy of humanannotated data, although it was reported that models trained on data generated in a similar manner outperform models trained on real-world data in certain tasks @xcite . in addition, it should also be noted that predominantly only one model, gpt-4o, is used for data synthesis. this lack of variation in model choice may limit the diversity of the synthetic data, which could in turn potentially reinforce the intrinsic biases of the model in question. future directions could include creating and utilizing training data in large quantities, as well as optimizing prompting techniques to obtain higherquality training data from llms. another viable approach would be to adopt more advanced stateof-the-art models for either data creation or finetuning.