Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large
  Language Models by Extrapolating Errors from Small Models
*Data Synthesis* is a promising way to train a small model with very little
labeled data. One approach for data synthesis is to leverage the rich knowledge
from large language models to synthesize pseudo training examples for small
models, making it possible to achieve both data and compute efficiency at the
same time. However, a key challenge in data synthesis is that the synthesized
dataset often suffers from a large distributional discrepancy from the *real
task* data distribution. Thus, in this paper, we propose *Synthesis Step by
Step* (**S3**), a data synthesis framework that shrinks this distribution gap
by iteratively extrapolating the errors made by a small model trained on the
synthesized dataset on a small real-world validation dataset using a large
language model. Extensive experiments on multiple NLP tasks show that our
approach improves the performance of a small model by reducing the gap between
the synthetic dataset and the real data, resulting in significant improvement
compared to several baselines: 9.48% improvement compared to ZeroGen and 2.73%
compared to GoldGen, and at most 15.17% improvement compared to the small model
trained on human-annotated data.
6535f2ff9860c1cb37002ae2
Ruida Wang
claimed_verified
2024-07-10T15:12:45.338Z
6618d1c3c1167c8d8702b19d
/avatars/24611ca6c4158f4978ac8476a87d8d9c.svg
Ruida WANG
user
RickyDeSkywalker
6535f2ff9860c1cb37002ae3
Wangchunshu Zhou
admin_assigned
2023-10-23T08:33:48.670Z
628c8598ef14f971b698107f
/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg
Zhou
user
Wangchunshu
6535f2ff9860c1cb37002ae4
Mrinmaya Sachan
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large
  Language Models by Extrapolating Errors from Small Models
*Data Synthesis* is a promising way to train a small model with very little
labeled data. One approach for data synthesis is to leverage the rich knowledge
from large language models to synthesize pseudo training examples for small
models, making it possible to achieve both data and compute efficiency at the
same time. However, a key challenge in data synthesis is that the synthesized
dataset often suffers from a large distributional discrepancy from the *real
task* data distribution. Thus, in this paper, we propose *Synthesis Step by
Step* (**S3**), a data synthesis framework that shrinks this distribution gap
by iteratively extrapolating the errors made by a small model trained on the
synthesized dataset on a small real-world validation dataset using a large
language model. Extensive experiments on multiple NLP tasks show that our
approach improves the performance of a small model by reducing the gap between
the synthetic dataset and the real data, resulting in significant improvement
compared to several baselines: 9.48% improvement compared to ZeroGen and 2.73%
compared to GoldGen, and at most 15.17% improvement compared to the small model
trained on human-annotated data.
Synthesis Step by Step (S3) iteratively refines synthesized pseudo training examples using a large language model to reduce distributional discrepancies and improve small model performance on NLP tasks.
data synthesis large language models pseudo training examples distributional discrepancy Synthesis Step by Step (S3) iterative extrapolation real-world validation dataset ZeroGen GoldGen