GenAI4ELab/papercli-papers huggingface dataset:
gowithnlp at SemEval-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification
Authors: Bo Wang; Ruichen Song; Xiangyu Wang; Ge Shi; Linmei Hu; Heyan Huang; Chong Feng
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our system for Subtask 10 of Entity Framing, which focuses on assigning one or more hierarchical roles to named entities in news articles. Our approach iteratively refines prompts and utilizes the Entity-Centric Chain of Thought to complete the task. Specifically, to minimize ambiguity in label definitions, we use the model’s predictions as supervisory signals, iteratively refining the category definitions. Furthermore, to minimize the interference of irrelevant information during inference, we incorporate entity-related information into the CoT framework, allowing the model to focus more effectively on entity-centric reasoning. Our system achieved the highest ranking on the leaderboard in the Russian main role classification and the second in English, with an accuracy of 0.8645 and 0.9362, respectively. We discuss the impact of several components of our multilingual classification approach, highlighting their effectiveness.
NCL-NLP at SemEval-2025 Task 11: Using Prompting engineering framework and Low Rank Adaptation of Large Language Models for Multi-label Emotion Detection
Authors: Kun Lu
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The paper presented a prompt engineer framework to further improve the performance of generative models on multi-label classification tasks which released in SemEval-2025 Task 11 Track A. This task is used to predict the presence of all emotions contained in a text segment, namely joy, fear, anger, surprise, and sadness. The generative large language model, fine-tuned with instructions, can accomplish multi-label classification tasks to a certain extent; however, there is still room for improvement in its correctness and accuracy. To address these problems, we proposed a framework for prompt engineering to further enhance performance, while using the specifications of instruction fine-tuning to generate the model’s response results. Compared to the method of fine-tuning using simple instructions, our system improved the overall macro F1 score by 0.3. There has been a significant improvement in the accuracy of each individual category. In the final ranking, a good performance was achieved. Nevertheless, the system still has certain issues, as the results of local validation may differ from the results of official competitions. This could be due to the training samples being insufficient and unbalanced. Therefore, the system can still improve its performance through feature engineering and other data enhancement methods.
BERTastic at SemEval-2025 Task 10: State-of-the-Art Accuracy in Coarse-Grained Entity Framing for Hindi News
Authors: Tarek Mahmoud; Zhuohan Xie; Preslav Nakov
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We describe our system for SemEval-2025 Task 10 Subtask 1 on coarse-grained entity framing in Hindi news, exploring two complementary strategies. First, we experiment with LLM prompting using GPT-4o, comparing hierarchical multi-step prompting with native single-step prompting for both main and fine-grained role prediction. Second, we conduct an extensive study on fine-tuning XLM-R, analyzing different context granularities (full article, paragraph, or sentence-level entity mentions), monolingual vs. multilingual settings, and main vs. fine-grained role labels. Our best system, trained on fine-grained role annotations across languages using sentence-level context, achieved 43.99% exact match, 56.56 % precision, 47.38% recall, and 51.57% F1-score. Notably, our system set a new state-of-the-art for main role prediction on Hindi news, achieving 78.48 % accuracy - outperforming the next best model at 76.90%, as per the official leaderboard. Our findings highlight effective strategies for entity framing in multilingual and low-resource settings.
Heimerdinger at SemEval-2025 Task 11: A Multi-Agent Framework for Perceived Emotion Detection in Multilingual Text
Authors: Zeliang Tong; Zhuojun Ding; Yingjia Li
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents our system developed for the SemEval-2025 Task 11: Text-Based Emotion Detection (TBED) task, which aims to identify the emotions perceived by the majority of people from a speaker’s short text. We introduce a multi-agent framework for emotion recognition, comprising two key agents: the Emotion Perception Profiler, which identifies emotions in text, and the Intensity Perception Profiler, which assesses the intensity of those emotions. We model the task using both generative and discriminative approaches, leveraging BERT series and large-scale generative language models (LLMs). A multi-system collaboration mechanism is employed to further enhance the accuracy, stability, and robustness. Additionally, we incorporate cross-lingual knowledge transfer to improve performance in diverse linguistic scenarios. Our method demonstrates superior results in emotion detection and intensity prediction across multiple subtasks, highlighting its effectiveness, especially in language adaptability.
Cyber for AI at SemEval-2025 Task 4: Forgotten but Not Lost: The Balancing Act of Selective Unlearning in Large Language Models
Authors: Dinesh Srivasthav P; Bala Mallikarjunarao Garlapati
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Large Language Models (LLMs) face significant challenges in maintaining privacy, ethics, and compliance, when sensitive or obsolete data must be selectively removed. Retraining these models from scratch is computationally infeasible, necessitating efficient alternatives. As part of the SemEval 2025 Task 4, this work focuses on the application of selective unlearning in LLMs to address this challenge. In this paper, we present our experiments and findings, primarily leveraging global weight modification to achieve an equilibrium between effectiveness of unlearning, knowledge retention, and target model’s post-unlearning utility. We also detail the task-specific evaluation mechanism, results, and challenges. Our algorithms have achieved an aggregate score of 0.409 and 0.389 on the test set for 7B and 1B target models, respectively, demonstrating promising results in verifiable LLM unlearning.
NCLTeam at SemEval-2025 Task 10: Enhancing Multilingual, multi-class, and Multi-Label Document Classification via Contrastive Learning Augmented Cascaded UNet and Embedding based Approaches
Authors: Shu Li; George Williamson; Huizhi Liang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The SemEval 2025 Task 10 Subtask2 presents a multi-task multi-label text classification challenge. The task requires systems to classify documents simultaneously across three distinct topics, the Climate Change(CC), the Ukraine Russia War(URW), and others. Several challenge were identified, including the instinct distinct of topics, the imbalance of categories, the insufficient samples, and the different distribution of develop set and test set. To address these challenges, two deep learning model have been implemented. One of the approach is the Contrastive learning augmented Cascaded UNet model(CCU), which employs a cascaded architecture to jointly process all subtasks. This model incorporates an UNet-style architecture to classify embeddings extracted by the base text encoder. A domain adaption method was implemented to facilitate joint learning across different document topics. We address the data insufficiency through contrastive learning and mitigate data imbalance using asymmetric loss function. We also implemented a shallow machine learning model. In this approach, transformer encoder models were applied to extract text embedding from various aspect, then deploy machine learning method to do the classification and compared with the base line. The UNet-style model achieves the highest f1 sample at 0.365 on the test set of 5th place compared with all approaches on leader board. Our source code developed for this paper are available at
DUTtask10 at SemEval-2025 Task 10: ThoughtFlow: Hierarchical Narrative Classification via Stepwise Prompting
Authors: Du Py; Huayang Li; Liang Yang; Zhang Shaowu
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our system for SemEval-2025 Task 10: Hierarchical Narrative Classification. We propose a two-step hierarchical approach that combines generative reasoning and fine-tuning for sub-narrative classification. The main techniques of our system are: 1) leveraging a large pre-trained model to generate a reasoning process for better context understanding, 2) fine-tuning the model for precise sub-narrative categorization, 3) using a multi-label classification strategy for more accurate sub-narrative identification, and 4) incorporating data augmentation to increase the diversity and robustness of the training data. Our system ranked 1st in Subtask 2 for Hindi, achieving an F1 macro coarse score of 0.56900 and an F1 samples score of 0.53500. The results demonstrate the effectiveness of our approach in classifying narratives and sub-narratives in a multilingual setting, with the additional benefit of enhanced model performance through data augmentation.
Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification
Authors: Niloofar Ranjbar; Hamed Baghbani
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa’s emotion classification performance. By incorporating explanatory context, our method improves F1-scores, particularly for emotions like fear, joy, and sadness, and outperforms text-only models. The addition of explanatory content helps resolve ambiguity, addresses challenges like overlapping emotional cues, and enhances multi-label classification, marking a significant advancement in emotion detection tasks.
LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles
Authors: Egil Rønningstad; Gaurav Negi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Our contribution to the SemEval shared task 10, subtask 1 on entity framing, tackles the challenge of providing the necessary segments from longer documents as context for classification with a masked language model. We show how simple entity-oriented heuristics for context selection and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models.
CCNU at SemEval-2025 Task 3: Leveraging Internal and External Knowledge of Large Language Models for Multilingual Hallucination Annotation
Authors: Xu Liu; Guanyi Chen
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: We present the system developed by the Central China Normal University (CCNU) team for the Mu-SHROOM shared task, which focuses on identifying hallucinations in question-answering systems across 14 different languages. Our approach leverages multiple Large Language Models (LLMs) with distinct areas of expertise, employing them in parallel to annotate hallucinations, effectively simulating a crowdsourcing annotation process. Furthermore, each LLM-based annotator integrates both internal and external knowledge related to the input during the annotation process. Using the open-source LLM DeepSeek-V3, our system achieves the top ranking (#1) for Hindi data and secures a Top-5 position in seven other languages. In this paper, we also discuss unsuccessful approaches explored during our development process and share key insights gained from participating in this shared task.
ClaimCatchers at SemEval-2025 Task 7: Sentence Transformers for Claim Retrieval
Authors: Rrubaa Panchendrarajan; Rafael Frade; Arkaitz Zubiaga
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Retrieving previously fact-checked claims from verified databases has become a crucial area of research in automated fact-checking, given the impracticality of manual verification of massive online content. To address this challenge, SemEval 2025 Task 7 focuses on multilingual previously fact-checked claim retrieval. This paper presents the experiments conducted for this task, evaluating the effectiveness of various sentence transformer models—ranging from 22M to 9B parameters—in conjunction with retrieval strategies such as nearest neighbor search and reranking techniques. Further, we explore the impact of learning context-specific text representation via finetuning these models. Our results demonstrate that smaller and medium-sized models, when optimized with effective finetuning and reranking, can achieve retrieval accuracy comparable to larger models, highlighting their potential for scalable and efficient misinformation detection.
NEKO at SemEval-2025 Task 4: A Gradient Ascent Based Machine Unlearning Strategy
Authors: Chi Kuan Lai; Yifei Chen
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: The power and wide application of large language models (LLMs) has brought the concerns on its risk of leaking private or sensitive information. However, retraining the modules is expensive and impractical, which introduces machine unlearning - removing specific information from language models while preserving general utility. Task 4 at SemEval 2025 consists of a shared task with this exact objective. We present an approach which combines gradient ascent-based forgetting with Kullback-Leibler (KL) divergence-based retention, applied to a 1-billion-parameter causal language model. Despite achieving effective forgetting, the system struggles with maintaining model utility. Our experiments reveal critical trade-off between unlearning effectiveness and performance preservation, highlighting challenges in practical machine unlearning implementations.
Team Unibuc - NLP at SemEval-2025 Task 11: Few-shot text-based emotion detection
Authors: Claudiu Creanga; Teodor - George Marchitan; Liviu Dinu
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset.
YNU-HPCC at SemEval-2025 Task 2: Local Cache and Online Retrieval-Based method for Entity-Aware Machine Translation
Authors: Hao Li; Jin Wang; Xuejie Zhang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper presents methods for {textbf{SemEval-2025 Task 11}} on text-based emotion detection across three tracks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. We apply approaches such as supervised fine-tuning, preference-based reinforcement learning, and few-shot learning to enhance performance. Our combined strategies result in improved accuracy, particularly in multi-label and cross-lingual emotion detection, demonstrating the effectiveness of these methods in diverse linguistic settings.
TechSSN3 at SemEval-2025 Task 9: Food Hazard and Product Detection - Category Identification and Vector Prediction
Authors: Rajalakshmi Sivanaiah; Karpagavalli S; Karthikeyan S; Krithika C
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: Food safety is a critical global concern, and timely detection of food-related hazards is essential for public health and economic stability. The automated detection of food hazards from textual data can enhance food safety monitoring by enabling early identification of potential risks. In the Food Hazard Detection task, we address two key challenges: (ST1) food hazard-category and product-category classification and (ST2) food hazard and product vector detection. For ST1, we employ BertForSequenceClassification, leveraging its powerful contextual understanding for accurate food hazard classification. For ST2, we utilize a Random Forest Classifier, which effectively captures patterns in the extracted features for food hazard and product vector detection. This paper presents the results of the TechSSN3 team at SemEval-2025 Food Hazard Detection Task .
MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps
Authors: Maximiliano Hormazábal Lagos; Álvaro Bueno Sáez; Héctor Cerezo - Costas; Pedro Alonso Doval; Jorge Alcalde Vesteiro
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In this paper we expose our approach to solve the SemEval 2025 Task 8: Question-Answering over Tabular Data challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language instructions in the form of steps to follow in order to get the answer, translating these instructions to code, running it and handling potential errors or exceptions. These steps use open source LLMs and fine grained optimized prompts for each task (step). With this approach, we achieved a score of 70.50% for subtask 1.
CYUT at SemEval-2025 Task 6: Prompting with Precision – ESG Analysis via Structured Prompts
Authors: Shih - Hung Wu; Z h i - H o n g Lin; Ping - Hsuan Lee
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: In response to the increasing need for efficientESG verification, we propose an innovativeNLP framework that automates the evaluationof corporate sustainability claims. Ourmethod integrates Retrieval-Augmented Generation,Chain-of-Thought reasoning, and structuredprompt engineering to effectively processand classify diverse, multilingual ESG disclosures.Evaluated under the SemEval-2025PromiseEval competition, our system achievedtop-tier performance—securing first place onthe public English leaderboard, excelling in theFrench track, and delivering marked improvementsover conventional machine learning approaches.These results highlight the framework’spotential to offer a scalable, transparent,and robust solution for corporate ESG assessment.
Angeliki Linardatou at SemEval-2025 Task 11: Multi-label Emotion Detection
Authors: Angeliki Linardatou; Paraskevi Platanou
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This study, competing in SemEval 2025 Task 11 - Track A, detects anger, surprise, joy, fear, and sadness. We propose a hybrid approach combining fine-tuned BERT transformers, TF-IDF for lexical analysis, and a Voting Classifier (Logistic Regression, Random Forest, SVM, KNN, XG-Boost, LightGBM, CatBoost), with grid search optimizing thresholds. Our model achieves a macro F1-score of0.6864. Challenges include irony, ambiguity, and label imbalance. Future work will explore larger transformers, data augmentation, and cross-lingual adaptation. This research underscores the benefits of hybrid models, showing that combining deep learning with traditional NLP improves multi-label emotion detection.
YNWA_PZ at SemEval-2025 Task 11: Multilingual Multi-Label Emotion Classification
Authors: Mohammad Sadegh Poulaei; Mohammad Erfan Zare; Mohammad Reza Mohammadi; Sauleh Eetemadi
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper explores multilingual emotion classification across binary classification, intensity estimation, and cross-lingual detection tasks. To address linguistic variability and limited annotated data, we evaluate various deep learning approaches, including transformer-based embeddings and traditional classifiers. After extensive experimentation, language-specific embedding models were selected as the final approach, given their superior ability to capture linguistic and cultural nuances. Experiments on high- and low-resource languages demonstrate that this method significantly improves performance, achieving competitive macro-average F1 scores. Notably, in languages such as Tigrinya and Kinyarwanda for cross-lingual detection task, our approach achieved a second-place ranking, driven by the incorporation of advanced preprocessing techniques. Despite these advances, challenges remain due to limited annotated data in underrepresented languages and the complexity of nuanced emotional expressions. The study highlights the need for robust, language-aware emotion recognition systems and emphasizes future directions, including expanding multilingual datasets and refining models.
DUTir at SemEval-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention
Authors: Zekun Wang; Jingjie Zeng; Yingxu Li; Liang Yang; Hongfei Lin
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model’s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model’s overall performance.
Zuifeng at SemEval-2025 Task 9: Multitask Learning with Fine-Tuned RoBERTa for Food Hazard Detection
Authors: Dapeng Sun; Sensen Li; Yike Wang; Shaowu Zhang
Venue: ACL 2025 (Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025))

Abstract: This paper describes our system used in theSemEval-2025 Task 9 The Food Hazard Detec-tion Challenge. Through data processing thatremoves elements and shared multi-task archi-tecture improve the performance of detection.Without complex architectural modificationsthe proposed method achieves competitive per-formance with 0.7835 Marco F1-score on sub-task 1 and 0.4712 Marco F1-score on sub-task2. Comparative experiments reveal that jointprediction outperforms separate task trainingby 1.3% F1-score, showing the effectiveness ofmulti-task learning of this challenge