mlsanityData to 14 Oct 2026

Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Data

arXiv 2503.24062 ↗First posted 31 Mar 2025cs.CL cs.AI cs.LGcited by 2

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Collecting high-quality training data is essential for fine-tuning Large Language Models (LLMs). However, acquiring such data is often costly and time-consuming, especially for non-English languages such as Italian. Recently, researchers have begun to explore the use of LLMs to generate synthetic datasets as a viable alternative. This study proposes a pipeline for generating synthetic data and a comprehensive approach for investigating the factors that influence the validity of synthetic data generated by LLMs by examining how model performance is affected by metrics such as prompt strategy, text length and target position in a specific task, i.e. inclusive language detection in Italian job advertisements. Our results show that, in most cases and across different metrics, the fine-tuned models trained on synthetic data consistently outperformed other models on both real and synthetic test datasets. The study discusses the practical implications and limitations of using synthetic data for language detection tasks with LLMs.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 17 of its 31 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

17 of 31 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications11 Oct
  2. 2024Efficacy of Synthetic Data as a Benchmark18 Sep
  3. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations11 Oct

Other references · 14

  1. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  2. 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 270
  3. 2023LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Modelscited by 196
  4. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  5. 2022Prompting Is Programming: A Query Language for Large Language Modelscited by 110
  6. 2024Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Modelscited by 99
  7. 2018Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Datacited by 94
  8. 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 69
  9. 2024An Investigation of Large Language Models for Real-World Hate Speech Detectioncited by 20
  10. 2024JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matchingcited by 12
  11. 2024HateTinyLLM : Hate Speech Detection Using Tiny Large Language Modelscited by 7
  12. 2024Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detectioncited by 5
2 more
  1. 2022Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Modelscited by 3
  2. 2023Hate Speech Detection in Limited Data Contexts using Synthetic Data Generationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025DiscussLLM: Teaching Large Language Models When to Speakcited by 2
  2. 2026The Gold in Bias: Maturing the AI Design Process through Verificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024On the Diversity of Synthetic Data and its Impact on Training Large Language Models
  2. 2026Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
  3. 2024Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
  4. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Code
  5. 2025Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls
  6. 2025Scaling Laws of Synthetic Data for Language Models
  7. 2024Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective
  8. 2023Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science