This paper presents our approach in the COLING2025-CoMeDi task in 7 languages, focusing on sub-task 1: Median Judgment Classification with Ordinal Word-in-Context Judgments (OGWiC). Specifically, we need to determine the meaning relation of one word in two different contexts and classify the input into 4 labels. To address sub-task 1, we implement and investigate various solutions, including (1) Stacking, Averaged Embedding techniques with a multilingual BERT-based model; and (2) utilizing a Natural Language Inference approach instead of a regular classification process. All the experiments were conducted on the P100 GPU from the Kaggle platform. To enhance the context of input, we perform Improve Known Data Rate and Text Expansion in some languages. For model focusing purposes Custom Token was used in the data processing pipeline. Our best official results on the test set are 0.515, 0.518, and 0.524 in terms of Krippendorff’s α score on task 1. Our participation system achieved a Top 3 ranking in task 1. Besides the official result, our best approach also achieved 0.596 regarding Krippendorff’s α score on Task 1.

This paper presents our system developed for SciHal2025: Hallucination Detection for Scientific Content. The primary goal of this task is to detect hallucinated claims based on the corresponding reference. Our methodology leverages strategic prompt engineering to enhance LLMs’ ability to accurately distinguish between factual assertions and hallucinations in scientific contexts. Moreover, we discovered that aggregating the fine-grained classification results from the more complex subtask (subtask 2) into the simplified label set required for the simpler subtask (subtask 1) significantly improved performance compared to direct classification for subtask 1. This work contributes to the development of more reliable AI-powered research tools by providing a systematic framework for hallucination detection in scientific content.