ELSA: A Style Aligned Dataset for Emotionally Intelligent Language Generation
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Abstract (arXiv)
Advancements in emotion aware language processing increasingly shape vital NLP applications ranging from conversational AI and affective computing to computational psychology and creative content generation. Existing emotion datasets either lack emotional granularity or fail to capture necessary stylistic diversity, limiting the advancement of effective emotion conditioned text generation systems. Seeking to bridge this crucial gap between granularity and style diversity, this paper introduces a novel systematically constructed dataset named ELSA Emotion and Language Style Alignment Dataset leveraging fine grained emotion taxonomies adapted from existing sources such as dair ai emotion dataset and GoEmotions taxonomy. This dataset comprises multiple emotionally nuanced variations of original sentences regenerated across distinct contextual styles such as conversational, formal, poetic, and narrative, using advanced Large Language Models LLMs. Rigorous computational evaluation using metrics such as perplexity, embedding variance, readability, lexical diversity, and semantic coherence measures validates the datasets emotional authenticity, linguistic fluency, and textual diversity. Comprehensive metric analyses affirm its potential to support deeper explorations into emotion conditioned style adaptive text generation. By enabling precision tuned emotionally nuanced language modeling, our dataset creates fertile ground for research on fine grained emotional control, prompt driven explanation, interpretability, and style adaptive expressive language generation with LLMs.
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References · 14
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 486
- 2023Large Language Models Understand and Can be Enhanced by Emotional Stimulicited by 146
- 2018Disentangled Representation Learning for Non-Parallel Text Style Transfercited by 127
- 2023Guiding Large Language Models via Directional Stimulus Promptingcited by 95
- 2022AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversationcited by 79
- 2024EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysiscited by 62
- 2022Augmenting Interpretable Models with LLMs during Trainingcited by 38
- 2021Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysiscited by 22
- 2024Make Me Happier: Evoking Emotions Through Image Diffusion Modelscited by 16
- 2025Prompt Sentiment: The Catalyst for LLM Changecited by 10
- 2024Recent Advancement of Emotion Cognition in Large Language Modelscited by 9
- 2023Emotion-Conditioned Text Generation through Automatic Prompt Optimizationcited by 6
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- 2025From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Models
- 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
- 2026Acoustic and Semantic Modeling of Emotion in Spoken Language ↗
- 2025Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting
- 2026Controlling Chat Style in Language Models via Single-Direction Editing
- 2024Rethinking Emotion Annotations in the Era of Large Language Models