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# dataset generation

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  1. arXiv

    Invent-A-Dataset generates post-training datasets from zero data, reporting 17% relative quality gains and 19% relative diversity gains

    This technical report introduces Invent-A-Dataset, a prompt-based system that goes from a dataset description to realistic and large-scale post-training datasets; evaluated in a zero-data regime against five frontier model APIs (Anthropic, Google, OpenAI, DeepSeek, Zai) across eight task types and dataset sizes up to 20K samples, it reports the highest quality (17% relative gains) and the most diverse samples (19% relative gains), with its diversity advantage widening with scale (from parity at 200 samples to 37% relative gains at 20K samples), translating into downstream training gains and higher rankings for its fine-tune across post-trained model architectures.

    Shivalika Singh（First author）
    Invent-A-Datasetdataset generationzero data regimePost-training
  2. arXiv

    FoMo turns the forking moment of a diffusion trajectory into automatic perceptual-distance labels, reaching 0.733 SROCC on PIPAL and beating metrics trained on human-annotated data

    The work proposes FoMo: forward-noising a reference image to a sampled timestep and then denoising it independently, using that forking moment as a pointwise perceptual-distance label, so training data can be generated with no human annotation, and trains reference-based IQA metrics with a RankNet-style global ranking objective; across four benchmarks and seven backbones it achieves the best average performance, with LPIPS-Alex reaching 0.733 SROCC on PIPAL versus 0.577 for the same architecture trained on human-annotated KADID-10k labels.

    Jaihyun Lew（First author）
    FoMoReference-based Image Quality Assessmentdataset generationDiffusion ModelsLearning to Rank

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