Lotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 27 Feb 2025.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 486
- 2024EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysiscited by 62
- 2023DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversationscited by 30
- 2025BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languagescited by 27
- 2024Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understandingcited by 17
- 2024VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoningcited by 16
- 2021Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognitioncited by 10
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