Data Value in the Age of Scaling: Understanding LLM Scaling Dynamics Under Real-Synthetic Data Mixtures

• URL: http://arxiv.org/abs/2511.13640v1
• Date: Mon, 17 Nov 2025 17:53:12 GMT
• Title: Data Value in the Age of Scaling: Understanding LLM Scaling Dynamics Under Real-Synthetic Data Mixtures
• Authors: Haohui Wang, Jingyuan Qi, Jianpeng Chen, Jun Wu, Lifu Huang, Lecheng Zheng, Kevin Choi, Balaji Veeramani, Edward Bowen, Alison Hu, Tyler Cody, Dawei Zhou,
• Abstract summary: Large language models (LLMs) are built on datasets that blend real and synthetic data.<n> synthetic data offers scalability and cost-efficiency, but it often introduces systematic distributional discrepancies.<n>We propose an effective yet efficient data valuation method that scales to large-scale datasets.
• Score: 32.89034139737846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of large language models (LLMs) is fueled by the growing reliance on datasets that blend real and synthetic data. While synthetic data offers scalability and cost-efficiency, it often introduces systematic distributional discrepancies, particularly underrepresenting long-tail knowledge due to truncation effects from data generation mechanisms like top-p sampling, temperature scaling, and finite sampling. These discrepancies pose fundamental challenges in characterizing and evaluating the utility of mixed real-synthetic datasets. In this paper, we identify a three-phase scaling behavior characterized by two breakpoints that reflect transitions in model behavior across learning head and tail knowledge. We further derive an LLM generalization bound designed for real and synthetic mixtures, revealing several key factors that govern their generalization performance. Building on our theoretical findings, we propose an effective yet efficient data valuation method that scales to large-scale datasets. Comprehensive experiments across four tasks, including image classification, sentiment classification, instruction following, and complex reasoning, demonstrate that our method surpasses state-of-the-art baselines in data valuation with significantly low computational cost.

Related papers

• Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation [3.3637719592955526]
  We explore the integration of real and synthetic data to enhance LPR performance.<n>Massive incorporation of synthetic data substantially boosts model performance in both intra- and cross-dataset scenarios.<n>Experiments underscore the efficacy of synthetic data in mitigating challenges posed by limited training data.
  arXiv  Detail & Related papers  (2026-01-12T15:52:52Z)
• Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression [53.39128997308138]
  We introduce information capacity, a measure of model efficiency based on text compression performance.<n> Empirical evaluations on mainstream open-source models show that models of varying sizes within a series exhibit consistent information capacity.<n>A distinctive feature of information capacity is that it incorporates tokenizer efficiency, which affects both input and output token counts.
  arXiv  Detail & Related papers  (2025-11-11T10:07:32Z)
• Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference [89.5628648718851]
  Causal inference is essential for developing and evaluating medical interventions.<n>Real-world medical datasets are often difficult to access due to regulatory barriers.<n>We present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine.
  arXiv  Detail & Related papers  (2025-10-21T16:16:00Z)
• Scaling Laws of Synthetic Data for Language Models [125.41600201811417]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets.<n>Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis [0.0]
  Machine learning in materials science faces challenges due to limited experimental data.<n>We propose strategies that utilize large language models (LLMs) to enhance machine learning performance.
  arXiv  Detail & Related papers  (2025-03-06T16:04:01Z)
• Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information [43.44508080585033]
  We introduce conditional mutual information (CMI) to assess the class-aware complexity of a dataset.<n>We minimize the distillation loss while constraining the class-aware complexity of the synthetic dataset.
  arXiv  Detail & Related papers  (2024-12-13T08:10:47Z)
• Marginal Causal Flows for Validation and Inference [3.547529079746247]
  Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging.<n>We introduce Frugal Flows, a novel likelihood-based machine learning model that uses normalising flows to flexibly learn the data-generating process.<n>We demonstrate the above with experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-11-02T16:04:57Z)
• On the Diversity of Synthetic Data and its Impact on Training Large Language Models [34.00031258223175]
  Large Language Models (LLMs) have accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. We study the downstream effects of synthetic data diversity during both the pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2024-10-19T22:14:07Z)
• SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data [78.70620682374624]
  We introduce SynFER, a novel framework for synthesizing facial expression image data based on high-level textual descriptions.<n>To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique and a pseudo-label generator.<n>Results validate the efficacy of our approach and the synthetic data.
  arXiv  Detail & Related papers  (2024-10-13T14:58:21Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.