mlsanityData to 14 Oct 2026

Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models

arXiv 2602.05106 ↗First posted 4 Feb 2026cs.CL cs.LG stat.MLcited by 0

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

Abstract (arXiv)

Scarcity of labeled training data remains the long pole in the tent for building performant language technology and generative AI models. Transformer models -- particularly LLMs -- are increasingly being used to mitigate the data scarcity problem via synthetic data generation. However, because the models are black boxes, the properties of the synthetic data are difficult to predict. In practice it is common for language technology engineers to 'fiddle' with the LLM temperature setting and hope that what comes out the other end improves the downstream model. Faced with this uncertainty, here we propose Data Kernel Perspective Space (DKPS) to provide the foundation for mathematical analysis yielding concrete statistical guarantees for the quality of the outputs of transformer models. We first show the mathematical derivation of DKPS and how it provides performance guarantees. Next we show how DKPS performance guarantees can elucidate performance of a downstream task, such as neural machine translation models or LLMs trained using Contrastive Preference Optimization (CPO). Limitations of the current work and future research are also discussed.

Key facts. No citing papers in the map yet (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 35 of its 49 references that are in the map. First posted on arXiv 4 Feb 2026.

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35 of 49 references.

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Same direction, newest first

  1. 2025Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls2 Oct

Other references · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
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22 more
  1. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translationcited by 394
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Self-Consuming Generative Models Go MADcited by 250
  4. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 197
  5. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
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  12. 2019Curriculum Learning for Domain Adaptation in Neural Machine Translationcited by 59
  13. 2024Fairness Feedback Loops: Training on Synthetic Data Amplifies Biascited by 55
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  18. 2024Consistent estimation of generative model representations in the data kernel perspective spacecited by 10
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  20. 2023Synthetic Cross-language Information Retrieval Training Datacited by 8
  21. 2025Low-dimensional embeddings of high-dimensional datacited by 8
  22. 2024Debiasing Synthetic Data Generated by Deep Generative Modelscited by 3

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