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        <title>Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models</title>
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    <abstract>Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions and large-scale user texts. Despite the promising results, two key challenges remain: (1) manually written instructions are limited in diversity and quantity, making them insufficient to ensure comprehensive coverage of distilled knowledge; (2) large-scale user texts incur high computational cost, hindering the practicality of these methods. To this end, we introduce CompEffDist, a comprehensive and efficient distillation framework for sentiment analysis. Our framework consists of two key modules: attribute-based automatic instruction construction and difficulty-based data filtering, which correspondingly tackle the aforementioned challenges. Applying our method across multiple model series (Llama-3, Qwen-3, and Gemma-3), we enable 3B student models to match the performance of 20x larger teacher models on most tasks. In addition, our approach greatly outperforms baseline methods in data efficiency, attaining the same performance level with only 10% of the data. All codes are available at https://github.com/HITSZ-HLT/COMPEFFDIST.</abstract>
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%0 Conference Proceedings
%T Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models
%A Xie, Guangyu
%A Zhang, Yice
%A Bao, Jianzhu
%A Wang, Qianlong
%A Sun, Yang
%A Wang, Bingbing
%A Xu, Ruifeng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xie-etal-2025-comprehensive
%X Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions and large-scale user texts. Despite the promising results, two key challenges remain: (1) manually written instructions are limited in diversity and quantity, making them insufficient to ensure comprehensive coverage of distilled knowledge; (2) large-scale user texts incur high computational cost, hindering the practicality of these methods. To this end, we introduce CompEffDist, a comprehensive and efficient distillation framework for sentiment analysis. Our framework consists of two key modules: attribute-based automatic instruction construction and difficulty-based data filtering, which correspondingly tackle the aforementioned challenges. Applying our method across multiple model series (Llama-3, Qwen-3, and Gemma-3), we enable 3B student models to match the performance of 20x larger teacher models on most tasks. In addition, our approach greatly outperforms baseline methods in data efficiency, attaining the same performance level with only 10% of the data. All codes are available at https://github.com/HITSZ-HLT/COMPEFFDIST.
%R 10.18653/v1/2025.emnlp-main.1122
%U https://aclanthology.org/2025.emnlp-main.1122/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1122
%P 22081-22102

Markdown (Informal)

[Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models](https://aclanthology.org/2025.emnlp-main.1122/) (Xie et al., EMNLP 2025)

• Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models (Xie et al., EMNLP 2025)

ACL

• Guangyu Xie, Yice Zhang, Jianzhu Bao, Qianlong Wang, Yang Sun, Bingbing Wang, and Ruifeng Xu. 2025. Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22081–22102, Suzhou, China. Association for Computational Linguistics.