Farzi Data: Autoregressive Data Distillation

• URL: http://arxiv.org/abs/2310.09983v1
• Date: Sun, 15 Oct 2023 23:23:27 GMT
• Title: Farzi Data: Autoregressive Data Distillation
• Authors: Noveen Sachdeva, Zexue He, Wang-Cheng Kang, Jianmo Ni, Derek Zhiyuan Cheng, Julian McAuley
• Abstract summary: We study data distillation for auto-regressive machine learning tasks. We propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences.
• Score: 34.39112473620335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences -- Farzi Data -- which are optimized to maintain (if not improve) model performance compared to training on the full dataset. Under the hood, Farzi conducts memory-efficient data distillation by (i) deriving efficient reverse-mode differentiation of the Adam optimizer by leveraging Hessian-Vector Products; and (ii) factorizing the high-dimensional discrete event-space into a latent-space which provably promotes implicit regularization. Empirically, for sequential recommendation and language modeling tasks, we are able to achieve 98-120% of downstream full-data performance when training state-of-the-art models on Farzi Data of size as little as 0.1% of the original dataset. Notably, being able to train better models with significantly less data sheds light on the design of future large auto-regressive models, and opens up new opportunities to further scale up model and data sizes.

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