NCL - NLP at S em E val-2025 Task 11: Using Prompting engineering framework and Low Rank Adaptation of Large Language Models for Multi-label Emotion Detection (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.

## introduction in this article, we present an approach for addressing the semeval-2025 task 11 track a task which is focusing on perceiving emotion and focusing on determining what emotion most people will think the speaker may be feeling given a sentence or short text snippet uttered by the speaker. emotions that need to be accurately identified include joy, fear, surprise, sadness, and anger @xcite . emotion detection involves identifying and understanding human emotions through various methods including facial analysis, speech analysis, text analysis, and so on. the task focuses on text analysis, specifically sentiment analysis, is a method of emotion detection that uses natural language processing (nlp) to analyse written text and determine the sentiment or emotion expressed. this technique helps gauge public opinion, understand customer feedback, and detect emotions in online communication. sentiment analysis can determine if a text expresses a positive, negative, or neutral sentiment. it is useful for various applications, including social media monitoring, brand reputation management, and customer service @xcite . in this task, we use a generative large language model that has been fine-tuned with instructions. llms fine-tuned through instructions can provide answers to some extent. however, due to the uncertainty in the generation of language models, the answers often do not correspond to the specified categories of emotions, and the accuracy of the answers is not very high. therefore, we have designed a prompt engineering framework to help the model generate better responses and ensure that the model only outputs the specified categories of emotions. ## related work sentiment analysis involves various methods, which can be categorized into two primary approaches: machine learning-based, and deep learning-based methods @xcite . using machine learning to solve the problem of multi-label classification involves data preprocessing, including data collection, data cleaning, label encoding (one-hot encoding), and feature extraction (tf-idf and word2vec); selecting appropriate models, such as decision trees, random forests, support vector machines, or some ensemble models like xgboost @xcite . in addition, deep learning can also be used to solve multi-label classification problems. apart from some data preprocessing stages, deep learning can choose neural networks as models, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and transformers, among others. for example, adversarial networks and temporal convolution networks can be used for emotion recognition in man-figure 1 : overview of the system darin @xcite .for transformer architecture, the most representative ones, bert, gpt and t5 can be used for language understanding tasks and text generation tasks, respectively. bert can extract the embedding representation of text and then perform multi-text classification through a linear classifier @xcite . on the other hand, a generative model fine-tuned with instructions can complete text classification tasks under specific instructions @xcite . due to the large number of parameters in such models and their training on vast amounts of data, they are also referred to as large language models. t5 is a complete transformer architecture, which has been used in previous research combined with chain of thought for sentiment classification @xcite . ## system overview our method can improve the performance of multilabel classification tasks by designing an effective prompt engineering framework and using lora fine-tuning techniques to constrain the output of generative models, preventing the generation of invalid classification results. ## results and discussions in this study, we compare the performance of two models: the baseline model with simple instruction and our model. as shown in in terms of overall performance, the prompt framework also surpasses the baseline in both micro f1 and macro f1 scores, with improvements of 0.021 and 0.027, respectively. these results suggest that the our system not only performs better on individual emotion classification tasks but also achieves a more balanced classification across all categories, as indicated by the macro f1 score. however, there are still some shortcomings, for example, the experiment was only validated in english and not tested in other languages. ## conclusion this article presents a new prompt framework for the task of text classification, further improved the ability to recognize various emotions based on a high baseline score. by adding elements such as character definitions, task descriptions, skill requirements, goal settings, constraints, workflows, examples, output format constraints, and startup instructions to the originally simple prompt, the proposed approach achieved improved results in the competition. however, there is still room for improvement in the anger category. furthermore, we can explore the hidden features contained in the data through feature engineering. for example, the notre dame cathedral is often associated with fires. external tools like wikidata can be used to determine if there are similar keywords in the text and then add relevant features to the prompt words. at the same time, we can also explore the application of discriminative llm @xcite for classification tasks.