从模型权重反推训练数据，用公开文本恢复模型性能
Approximating Language Model Training Data from Weights
- 基于梯度匹配，在公共文本中筛选与模型最契合的数据
- 在分类任务上将准确率从65%提升至80%，接近专家水平
- 适合想逆向理解模型训练数据的研究者或安全评估人员
现代语言模型常开放权重但封闭训练数据。本文正式定义了从模型权重中近似训练数据的问题，并提出若干基线方法与评估指标。我们开发了一种基于梯度的方法，从大规模公开文本语料库中选择与模型最匹配的数据。即使原始训练数据完全未知，该方法仍能定位出少量公共网页文档，用于训练出性能接近原模型的模型。在AG News分类任务中，性能从随机选取数据的65%提升至80%，接近专家基准88%。在MSMARCO SFT微调模型上，困惑度从3.3降至2.3，接近专家LLAMA模型的2.0。
原文摘要 · Abstract (English)
Modern language models often have open weights but closed training data. We formalize the problem of data approximation from model weights and propose several baselines and metrics. We develop a gradient-based approach that selects the highest-matching data from a large public text corpus and show its effectiveness at recovering useful data given only weights of the original and finetuned models. Even when none of the true training data is known, our method is able to locate a small subset of public Web documents can be used to train a model to close to the original model performance given models trained for both classification and supervised-finetuning. On the AG News classification task, our method improves performance from 65% (using randomly selected data) to 80%, approaching the expert benchmark of 88%. When applied to a model trained with SFT on MSMARCO web documents, our method reduces perplexity from 3.3 to 2.3, compared to an expert LLAMA model's perplexity of 2.0.
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