Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large
Language Models by Extrapolating Errors from Small Models
- URL: http://arxiv.org/abs/2310.13671v1
- Date: Fri, 20 Oct 2023 17:14:25 GMT
- Title: Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large
Language Models by Extrapolating Errors from Small Models
- Authors: Ruida Wang, Wangchunshu Zhou, Mrinmaya Sachan
- Abstract summary: *Data Synthesis* is a promising way to train a small model with very little labeled data.
We propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap.
Our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data.
- Score: 69.76066070227452
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: *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.
Related papers
- Improving Grammatical Error Correction via Contextual Data Augmentation [49.746484518527716]
We propose a synthetic data construction method based on contextual augmentation.
Specifically, we combine rule-based substitution with model-based generation.
We also propose a relabeling-based data cleaning method to mitigate the effects of noisy labels in synthetic data.
arXiv Detail & Related papers (2024-06-25T10:49:56Z) - Expansive Synthesis: Generating Large-Scale Datasets from Minimal Samples [13.053285552524052]
This paper introduces an innovative Expansive Synthesis model that generates high-fidelity datasets from minimal samples.
We validate our Expansive Synthesis by training classifiers on the generated datasets and comparing their performance toversas trained on larger, original datasets.
arXiv Detail & Related papers (2024-06-25T02:59:02Z) - 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) - Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement [11.6055501181235]
We investigate the use of feedback on synthesized data to prevent model collapse.
We show that training from feedback-augmented synthesized data, either by pruning incorrect predictions or by selecting the best of several guesses, can prevent model collapse.
arXiv Detail & Related papers (2024-06-11T17:46:16Z) - TarGEN: Targeted Data Generation with Large Language Models [54.1093098278564]
TarGEN is a multi-step prompting strategy for generating high-quality synthetic datasets.
We augment TarGEN with a method known as self-correction empowering LLMs to rectify inaccurately labeled instances.
A comprehensive analysis of the synthetic dataset compared to the original dataset reveals similar or higher levels of dataset complexity and diversity.
arXiv Detail & Related papers (2023-10-27T03:32:17Z) - Private Synthetic Data Meets Ensemble Learning [15.425653946755025]
When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop.
We introduce a new ensemble strategy for training downstream models, with the goal of enhancing their performance when used on real data.
arXiv Detail & Related papers (2023-10-15T04:24:42Z) - Does Synthetic Data Make Large Language Models More Efficient? [0.0]
This paper explores the nuances of synthetic data generation in NLP.
We highlight its advantages, including data augmentation potential and the introduction of structured variety.
We demonstrate the impact of template-based synthetic data on the performance of modern transformer models.
arXiv Detail & Related papers (2023-10-11T19:16:09Z) - Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
We show how the generative process affects the downstream ML task.
We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
arXiv Detail & Related papers (2023-05-16T07:30:29Z) - Learning from aggregated data with a maximum entropy model [73.63512438583375]
We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
arXiv Detail & Related papers (2022-10-05T09:17:27Z)
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.