mlsanityData to 14 Oct 2026

Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis

arXiv 2405.05496 ↗First posted 9 May 2024cs.CLcited by 11

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Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on improving the performance of the target domain by fine-tuning domain-specific models (trained on source domains) based on the target domain dataset. Few works propose continual learning tasks for ABSA, which aim to learn the target domain's ability while maintaining the history domains' abilities. In this paper, we propose a Large Language Model-based Continual Learning (textttLLM-CL) model for ABSA. First, we design a domain knowledge decoupling module to learn a domain-invariant adapter and separate domain-variant adapters dependently with an orthogonal constraint. Then, we introduce a domain knowledge warmup strategy to align the representation between domain-invariant and domain-variant knowledge. In the test phase, we index the corresponding domain-variant knowledge via domain positioning to not require each sample's domain ID. Extensive experiments over 19 datasets indicate that our textttLLM-CL model obtains new state-of-the-art performance.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 22 of its 50 references that are in the map. First posted on arXiv 9 May 2024.

Builds on

22 of 50 references.

References · 22

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  6. 2023Orthogonal Subspace Learning for Language Model Continual Learningcited by 312
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10 more
  1. 2020Relational Graph Attention Network for Aspect-based Sentiment Analysiscited by 104
  2. 2021LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5cited by 104
  3. 2021A Unified Generative Framework for Aspect-Based Sentiment Analysiscited by 97
  4. 2021Achieving Forgetting Prevention and Knowledge Transfer in Continual Learningcited by 91
  5. 2016Aspect Level Sentiment Classification with Deep Memory Networkcited by 82
  6. 2018Transformation Networks for Target-Oriented Sentiment Classificationcited by 47
  7. 2021Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Taskscited by 46
  8. 2021CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Taskscited by 29
  9. 2018Exploiting Document Knowledge for Aspect-level Sentiment Classificationcited by 19
  10. 2021Continual Learning with Knowledge Transfer for Sentiment Classificationcited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 2.

11 papers.

  1. 2025EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecyclecited by 137
  2. 2024Recent Advances of Foundation Language Models-based Continual Learning: A Surveycited by 46
  3. 2025CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answeringcited by 37
  4. 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 25
  5. 2025Large Language Models for Subjective Language Understanding: A Surveycited by 9
  6. 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuningcited by 4
  7. 2025On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojiscited by 3
  8. 2025Reinforced Interactive Continual Learning via Real-time Noisy Human Feedbackcited by 3
  9. 2026Continual Fine-Tuning of Large Language Models via Program Memorycited by 1
  10. 2025Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysiscited by 0
  11. 2026Distributional sentiment modeling and anomaly detection for consumer complaint assessmentcited by 0