Austria

The Mu-SHROOM competition in the SemEval-2025 Task 3 aims to tackle the problem of detecting spans with hallucinations in texts, generated by Large Language Models (LLMs). Our developed system, submitted to this task, is a joint architecture that utilises Named Entity Recognition (NER), Retrieval-Augmented Generation (RAG) and LLMs to gather, compare and analyse information in the texts provided by organizers. We extract entities potentially capable of containing hallucinations with NER, aggregate relevant topics for them using RAG, then verify and provide a verdict on the extracted information using the LLMs. This approach allowed with a certain level of quality to find hallucinations not only in facts, but misspellings in names and titles, which was not always accepted by human annotators in ground truth markup. We also point out some inconsistencies within annotators spans, that perhaps affected scores of all participants.

ACL 2025

Advacheck at SemEval-2025 Task 3: Combining NER and RAG to Spot Hallucinations in LLM Answers

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

Understanding idioms in multimodal contexts poses significant challenges due to data scarcity, idiomatic ambiguity, and the need for effective alignment of visual and textual inputs. In this work, we introduce MIRA (Multimodal Idiom Recognition and Alignment), a training- free framework designed to address these challenges on the SemEval-2025 Task 1 (AdMIRe) benchmark. MIRA leverages powerful closed- source large language models (LLMs) and integrates three key innovations: bias correction via in-context learning, multi-step semantic- visual fusion, and a self-revision mechanism that iteratively refines its outputs through backward verification. By systematically processing and fusing multimodal inputs, MIRA generates high-quality, fine-grained image-text repre- sentations that enhance idiom comprehension across different languages and cultural contexts. Experimental evaluations in both English and Portuguese demonstrate that our approach achieves robust performance without the need for additional training, setting a new standard for multimodal idiom recognition.

PALI-NLP at SemEval 2025 Task 1: AdMIRe Advancing Multimodal Idiomaticity Representation

Question answering using Large Language Models has gained significant popularity in both everyday communication and at the workplace. However, certain tasks, such as querying tables, still pose challenges for commercial and open-source chatbots powered by advanced deep learning models. Addressing these challenges requires specialized approaches. 
During the SemEval-2025 Task 8 competition focused on tabular data, our solution achieved 86.21%accuracy and took 2nd place out of 100 teams. In this paper we present ten methods that significantly improve the baseline solution.

Samsung Research Poland at SemEval-2025 Task 8: LLM ensemble methods for QA over tabular data

Food contamination and associated illnesses represent significant global health challenges, leading to thousands of deaths worldwide. As the volume of food-related incident reports on web platforms continues to grow, there is a pressing demand for systems capable of detecting food hazards effectively. Furthermore, explainability in food risk detection is crucial for building trust in automated systems, allowing humans to validate predictions. SemEval-2025 Task 9 proposes a food hazard detection challenge to address this issue, utilizing content extracted from websites. This task is divided into two sub-tasks. Sub-task 1 involves classifying the type of hazard and product, while sub-task 2 focuses on identifying precise hazard and product ``vectors'' to offer detailed explanations for the predictions. This paper presents our participation in this task, where we introduce a transformer-based method. We fine-tune an enhanced version of the BERT transformer to process lengthy food incident reports. Additionally, we combine the transformer’s contextual embeddings to enhance its contextual representation for hazard and product ``vectors'' prediction. The experimental results reveal the competitive performance of our proposed method in this task, which achieved 7th place in both sub-tasks. We have released our code at https://github.com/AhmadMonir-CSECU/SemEval-2025_Task9.

CSECU-Learners at SemEval-2025 Task 9: Enhancing Transformer Model for Explainable Food Hazard Detection in Text

Multilingual hallucination detection stands as an underexplored challenge, which the Mu-SHROOM shared task seeks to address. In this work, we propose an efficient, training-free LLM prompting strategy that enhances detection by translating multilingual text spans into English. Our approach achieves competitive rankings across multiple languages, securing two first positions in low-resource languages.
The consistency of our results highlights the effectiveness of our translation strategy for hallucination detection, demonstrating its applicability regardless of the source language.

AILS-NTUA at SemEval-2025 Task 3: Leveraging Large Language Models and Translation Strategies for Multilingual Hallucination Detection

We present our submission to SemEval 2025 Task 8: Question Answering on Tabular Data, which challenges participants to develop systems capable of answering natural language questions on real-world tabular datasets. Our approach aims at generating Pandas code that can be run on such datasets to produce the desired answer. 
The approach consists in fine-tuning a Small Language Model (SLM) through Preference Optimization on both positive and negative examples generated by a teacher model. A base SLM is first elicited to produce the code to compute the answer to a question through a Chain of Thought (CoT) prompt. We performed extensive testing on the DataBench development set, exploring a variety of prompts, eventually settling on a detailed instruction prompt, followed by two-shot examples. 
Due to hardware constraints, the base model was an SLM with ≤ 8 billion parameters. We then fine-tuned the model through Odds Ratio Preference Optimization (ORPO) using as training data the code produced by a teacher model on the DataBench training set. The teacher model was GPT-4o, whose code was labeled preferred, while the code generated by the base model was rejected. This increased the accuracy on the development set from 71% to 85%.
Our method demonstrated robust performance in answering complex questions across diverse datasets, highlighting the effectiveness of combining small LLMs with supervised fine-tuning and automated code execution for tabular question answering.

Dataground at SemEval-2025 Task 8: Small LLMs and Preference Optimization for Tabular QA

In today's digital era, individuals convey their feelings, viewpoints, and perspectives across various platforms in nuanced and intricate ways. At times, these expressions can be challenging to articulate and interpret. Emotion recognition aims to identify the most relevant emotions in a text that accurately represent the author's psychological state. Despite its substantial impact on natural language processing (NLP), this task has primarily been researched only in high-resource languages. To bridge this gap, SemEval-2025 Task 11 introduces a multilingual emotion recognition challenge encompassing 32 languages, promoting broader linguistic inclusivity in emotion recognition. This paper presents our participation in this task, where we introduce a language-specific fine-tuned transformer-based system for emotion recognition and emotion intensity prediction. To enhance generalization, we incorporate a multi-sample dropout strategy. Our approach is evaluated across 11 languages, and experimental results demonstrate its competitive performance, achieving top-tier results in certain languages.

CSECU-Learners at SemEval-2025 Task 11: Multilingual Emotion Recognition and Intensity Prediction with Language-tuned Transformers and Multi-sample Dropout

Food safety incidents cause serious threats to public health, requiring efficient detection systems. This study contributes to SemEval 2025 Task 9: Food Hazard Detection by leveraging insights from existing literature and using multiple BERT-based models for multi-label classification of food hazards and product categories. Using a dataset of food recall notifications, we applied preprocessing techniques
to prepare data and address challenges like class imbalance. Experimental results show strong hazard classification performance on ensembled models such as DistilBERT, SciBERT, and DeBERTa but highlight product classification variability. Building on Tyagi et al. (Tyagi et al., 2023) and Madry et al.’s (Rebuffi et al., 2021) work, we explored strategies like ensemble modeling and data augmentation to improve accuracy and explainability, paving the way for scalable food safety solutions.

Habib University at SemEval-2025 Task 9: Using Ensemble Models for Food Hazard Detection

As the Large Language Model (LLM) gains widespread adoption, increasing attention has been given to the challenge of making LLM forget non-compliant data memorized during its pre-training. Machine Unlearning focuses on efficiently erasing sensitive information from LLM under limited computational resources. To advance research in this area, SemEval 2025 Task 4: "Unlearning Sensitive Content from Large Language Models" introduces three unlearning datasets and establishes a benchmark by evaluating both forgetting effectiveness and the preservation of standard capabilities. In this work, we propose a more controllable forgetting loss, Effective Unlearning Loss, and explore its integration with various techniques to achieve more efficient and controlled unlearning. Our system ultimately ranked 5th on the competition leaderboard.

iShumei-Chinchunmei at SemEval-2025 Task 4: A balanced forgetting and retention multi-task framework using effective unlearning loss

This paper describes our approach to the SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval on cross-lingual data. In this study, we developed a system to tackle the verified claim retrieval task, where the objective is to identify relevant, previously fact-checked claims from multilingual data based on a given input claim. We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated various embeddings, including e5-large, Jina embeddings, and the MPNet multilingual sentence transformer, to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed. Our team in the cross-lingual track scored s@10.

CAISA at SemEval-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval

The Unlearning Sensitive Content from Large Language Models task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradientbased unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we employ data chunking, splitting forget data into disjoint
partitions and merging them with cyclically sampled retain samples at a pre-defined
ratio. Our task-agnostic method achieves an outstanding forget-retain balance, ranking first on leaderboards and significantly outperforming baselines and competing systems.

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