mlsanityData to 7 Oct 2026

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

arXiv 2306.04140 ↗First posted 7 Jun 2023cs.CLcited by 121

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Abstract (arXiv)

Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. We first examine two approaches to diversify text generation: 1) logit suppression, which minimizes the generation of languages that have already been frequently generated, and 2) temperature sampling, which flattens the token sampling probability. We found that diversification approaches can increase data diversity but often at the cost of data accuracy (i.e., text and labels being appropriate for the target domain). To address this issue, we examined two human interventions, 1) label replacement (LR), correcting misaligned labels, and 2) out-of-scope filtering (OOSF), removing instances that are out of the user's domain of interest or to which no considered label applies. With oracle studies, we found that LR increases the absolute accuracy of models trained with diversified datasets by 14.4%. Moreover, we found that some models trained with data generated with LR interventions outperformed LLM-based few-shot classification. In contrast, OOSF was not effective in increasing model accuracy, implying the need for future work in human-in-the-loop text data generation.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). Builds on 35 of its 56 references that are in the map. First posted on arXiv 7 Jun 2023.

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35 of 56 references.

References · 35

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Citing papers per year: 2023: 12; 2024: 52; 2025: 41; 2026: 16.

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  2. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 240
  3. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
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  5. 2024Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arenacited by 76
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  8. 2023Large Language Models are Temporal and Causal Reasoners for Video Question Answeringcited by 51
  9. 2025Diverse Preference Optimizationcited by 48
  10. 2024EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Samplingcited by 42
  11. 2024RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMscited by 42
  12. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
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  4. 2023Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AIcited by 32
  5. 2024LLM-as-a-Judge & Reward Model: What They Can and Cannot Docited by 32
  6. 2024A Comprehensive Evaluation of Cognitive Biases in LLMscited by 32
  7. 2025Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Datasetcited by 32
  8. 2024Beyond Aesthetics: Cultural Competence in Text-to-Image Modelscited by 30
  9. 2025Roll the dice & look before you leap: Going beyond the creative limits of next-token predictioncited by 28
  10. 2025Building a Foundational Guardrail for General Agentic Systems via Synthetic Datacited by 26
  11. 2023The Efficiency Spectrum of Large Language Models: An Algorithmic Surveycited by 25
  12. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  13. 2025Multilingual Prompting for Improving LLM Generation Diversitycited by 25
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  16. 2023How Far Can We Extract Diverse Perspectives from Large Language Models?cited by 22
  17. 2024DataGen: Unified Synthetic Dataset Generation via Large Language Modelscited by 22
  18. 2023Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Modelscited by 20
  19. 2024Automatic Metrics in Natural Language Generation: A Survey of Current Evaluation Practicescited by 19
  20. 2024Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQAcited by 18
  21. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
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  24. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  25. 2023Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbotscited by 14
  26. 2025Beyond the Surface: Enhancing LLM-as-a-Judge Alignment with Human via Internal Representationscited by 14
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  28. 2024Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?cited by 13
  29. 2024Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understandingcited by 12
  30. 2024Scaling Parameter-Constrained Language Models with Quality Datacited by 12
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  32. 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
  33. 2024Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Contextcited by 11
  34. 2024Potential and Challenges of Model Editing for Social Debiasingcited by 10
  35. 2024MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translationcited by 10
  36. 2024Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Promptingcited by 10
  37. 2024Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarioscited by 10
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