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LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction

arXiv 2603.01778 ↗First posted 2 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

Training models for Aspect-Based Sentiment Analysis (ABSA) tasks requires manually annotated data, which is expensive and time-consuming to obtain. This paper introduces LA-ABSA, a novel approach that leverages Large Language Model (LLM)-generated annotations to fine-tune lightweight models for complex ABSA tasks. We evaluate our approach on five datasets for Target Aspect Sentiment Detection (TASD) and Aspect Sentiment Quad Prediction (ASQP). Our approach outperformed previously reported augmentation strategies and achieved competitive performance with LLM-prompting in low-resource scenarios, while providing substantial energy efficiency benefits. For example, using 50 annotated examples for in-context learning (ICL) to guide the annotation of unlabeled data, LA-ABSA achieved an F1 score of 49.85 for ASQP on the SemEval Rest16 dataset, closely matching the performance of ICL prompting with Gemma-3-27B (51.10), while requiring significantly lower computational resources.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 2 Mar 2026.

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References · 27

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15 more
  1. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 121
  2. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 99
  3. 2023Reasoning Implicit Sentiment with Chain-of-Thought Promptingcited by 79
  4. 2023MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Predictioncited by 49
  5. 2022Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentationcited by 29
  6. 2023Large language models for aspect-based sentiment analysiscited by 17
  7. 2024An Empirical Study on Challenges for LLM Application Developerscited by 17
  8. 2021Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysiscited by 10
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  10. 2025Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Predictioncited by 9
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  12. 2022Data Augmentation for Improving Emotion Recognition in Software Engineering Communicationcited by 4
  13. 2024Deep Learning Based Named Entity Recognition Models for Recipescited by 3
  14. 2024EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMscited by 3
  15. 2024ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialoguescited by 1

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