mlsanityData to 7 Oct 2026

From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis

arXiv 2312.04720 ↗First posted 7 Dec 2023cs.CL cs.AI cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation in sentiment analysis. We leverage ChatGPT's generative capabilities to create synthetic training data that significantly improves the performance of smaller models, making them competitive with, or even outperforming, their larger counterparts. This innovation enables models to be both efficient and effective, thereby reducing computational cost, inference time, and memory usage without compromising on quality. Our work marks a key advancement in the cost-effective development and deployment of robust sentiment analysis models.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 84 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

33 of 84 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  7. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  8. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  9. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  12. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
21 more
  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2017Synthetic and Natural Noise Both Break Neural Machine Translationcited by 304
  3. 2020MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformerscited by 240
  4. 2023ChatGPT: Jack of all trades, master of nonecited by 204
  5. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  6. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  7. 2020Syntactic Data Augmentation Increases Robustness to Inference Heuristicscited by 85
  8. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
  9. 2020KLEJ: Comprehensive Benchmark for Polish Language Understandingcited by 51
  10. 2023LLM-powered Data Augmentation for Enhanced Cross-lingual Performancecited by 51
  11. 2023Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPTcited by 41
  12. 2018Text Data Augmentation Made Simple By Leveraging NLP Cloud APIscited by 40
  13. 2020XtremeDistil: Multi-stage Distillation for Massive Multilingual Modelscited by 38
  14. 2019Improving short text classification through global augmentation methodscited by 33
  15. 2023ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPTcited by 32
  16. 2019Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchangecited by 19
  17. 2020Author's Sentiment Predictioncited by 18
  18. 2021XtremeDistilTransformers: Task Transfer for Task-agnostic Distillationcited by 18
  19. 2021ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classificationcited by 10
  20. 2023Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classificationcited by 6
  21. 2023Neural Machine Translation Data Generation and Augmentation using ChatGPTcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Healthcited by 5
  2. 2024Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Textscited by 3
  3. 2025Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classificationcited by 3
  4. 2024Masked Generative Story Transformer with Character Guidance and Caption Augmentationcited by 2
  5. 2025Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classificationcited by 2
  6. 2025Predicting stock prices with ChatGPT-annotated Reddit sentimentcited by 1