Lotus at S em E val-2025 Task 11: R o BERT a with Llama-3 Generated Explanations for Multi-Label Emotion Classification (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.

## introduction emotion classification plays a crucial role in natural language processing (nlp) for applications like sentiment analysis and emotion-aware dialogue systems @xcite . the challenge lies in accurately identifying emotions from text, which are often subtle, multi-faceted, and context-dependent. furthermore, emotions can be expressed simultaneously, making multi-label classification essential @xcite . despite advancements, emotion classification remains complex due to ambiguous emotional expressions and diverse contexts. early keyword-based methods struggled with generalizing across languages and expressions @xcite , and even modern transformer models face challenges with short or under-explained sentences, particularly in multi-label tasks @xcite . to address these challenges, we propose a novel approach using large language models (llms) to generate explanatory content, enhancing the understanding of ambiguous emotions. we fine-tuned a llama-3 model to generate context-rich explanations for each sentence, improving emotion classification, especially for multi-label settings. the ex-planatory context significantly boosts performance, as shown in prior work on llms and commonsense reasoning @xcite . the generated explanations were used with the original text to fine-tune roberta @xcite for multi-label emotion classification, enabling simultaneous emotion prediction. we participated in semeval 2025 task 11, subtask 1 @xcite , which focuses on multi-label emotion detection across multiple languages, including english. the dataset consists of social media text annotated by 122 annotators, with multi-label annotations for five emotions: anger, fear, joy, sadness, and surprise. the training set has 2,768 samples, the development set has 116, and the test set includes 2,767 samples, all with binary labels indicating the presence or absence of each emotion. our system, evaluated on english data, demonstrates that adding explanatory content significantly enhances model performance. specifically, the text + explanation model achieved a macro f1 score of 0.7396 with a standard deviation of 0.0016 over four runs, outperforming the text-only model, which had a macro f1 score of 0.7112 with a standard deviation of 0.0095 over four runs. this shows that explanatory context improves classification accuracy across different classes. the brighter dataset @xcite , which addresses the lack of high-quality emotion datasets, serves as the primary resource for this task. it provides labeled data in 28 languages and supports tackling challenges in emotion classification, such as ambiguous or complex emotional expressions. the code and data used in this study are available for reproducibility foot_0 . ## background this section provides an overview of the emotion detection task, dataset, and related works, focusing on the use of large language models (llms) and contextual information for improving emotion classification. ## system overview the task of multi-label emotion detection in text is inherently complex, especially when emotions are expressed simultaneously in a single sentence. to address this, our system employs a two-phase pipeline: first, generating explanatory content to enhance the understanding of ambiguous emotional expressions, followed by fine-tuning a roberta model for multi-label classification. ## experimental setup we evaluated our multi-label emotion detection approach by fine-tuning roberta with explanatory content generated by llama-3 on the brighter dataset. text preprocessing and tokenization were performed with the robertatokenizer from hugging face. in the first phase, llama-3 generated explanations, which were concatenated with the original text. in the second phase, both the original text and the generated explanations were tokenized together, allowing the model to learn the emotional context. for fine-tuning llama-3, we used 4-bit quantization and lora, with a batch size of 2, gradient accumulation steps of 4, and a learning rate of 1 × 10 -4 for 30 training steps. these explanations were then used in the second phase for emotion classification. roberta was fine-tuned with binary emotion labels (0 or 1) for each emotion in the dataset, using a batch size of 8, a learning rate of 5 × 10 -5 , and 3 epochs. the model performance was evaluated using precision, recall, and f1-scores, including both macro and micro f1-scores to assess multi-label classification. despite only 30 training steps, this light fine-tuning produced explanations that notably improved roberta's downstream performance. all experiments were conducted on kaggle's gpu resources, which provided the computational power for efficient fine-tuning. ## results in this section, we present the performance of our system, lotus, on the competition task. using the text + explanation (roberta) method, lotus achieved a score of 0.7319, outperforming the semeval baseline (0.7083), but falling short of the top score of 0.823. ranked 36th, lotus performed competitively, although there is still room for improvement to reach the top positions. ## discussion we introduced lotus, a multi-label emotion detection approach combining llama-3's generative explanations with roberta for emotion classification. this combination significantly improved performance, particularly for nuanced emotions like fear (f1: 0.8343), joy (f1: 0.7581), and sadness (f1: 0.7423), surpassing text-only models. integrating llama-3's explanations with roberta effectively balanced precision and recall, outperforming text only (llama-3), especially for complex emotions like fear and sadness, emphasizing the importance of explanatory context in capturing emotional nuances. although llama-3 was initially chosen, smaller models like mistral and qwen faced no significant gpu constraints on kaggle. after testing mistral, the results were nearly identical to llama-3, suggesting both models perform sim-ilarly when fine-tuned with roberta. further exploration of other models will provide more insights. for further illustration, ## conclusion and future work lotus showed that combining generative explanations with emotion detection models significantly improves emotion classification, particularly for ambiguous emotions. using llama-3 for explanation generation and roberta for emotion detection enhanced the system's ability to handle nuanced emotional expressions. future work will focus on improving detection of underrepresented emotions like anger, refining the explanation generation process, and addressing imbalanced datasets. expanding the model to support multiple languages and emotional contexts will enhance its generalizability. additionally, we plan to compare mistral with other models like qwen and conduct ablation studies to assess their contributions. further improvements will target challenging emotions like anger and surprise, with error analysis and model comparisons refining the system.