Generating Faithful Synthetic Data with Large Language Models: A Case
Study in Computational Social Science
- URL: http://arxiv.org/abs/2305.15041v1
- Date: Wed, 24 May 2023 11:27:59 GMT
- Title: Generating Faithful Synthetic Data with Large Language Models: A Case
Study in Computational Social Science
- Authors: Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora, Martin
Josifoski, Ashton Anderson, Robert West
- Abstract summary: We tackle a pervasive problem in synthetic data generation: its generative distribution often differs from the distribution of real-world data researchers care about.
We study three strategies to increase the faithfulness of synthetic data: grounding, filtering, and taxonomy-based generation.
We conclude this paper with some recommendations on how to generate high(er)-fidelity synthetic data for specific tasks.
- Score: 13.854807858791652
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have democratized synthetic data generation,
which in turn has the potential to simplify and broaden a wide gamut of NLP
tasks. Here, we tackle a pervasive problem in synthetic data generation: its
generative distribution often differs from the distribution of real-world data
researchers care about (in other words, it is unfaithful). In a case study on
sarcasm detection, we study three strategies to increase the faithfulness of
synthetic data: grounding, filtering, and taxonomy-based generation. We
evaluate these strategies using the performance of classifiers trained with
generated synthetic data on real-world data. While all three strategies improve
the performance of classifiers, we find that grounding works best for the task
at hand. As synthetic data generation plays an ever-increasing role in NLP
research, we expect this work to be a stepping stone in improving its utility.
We conclude this paper with some recommendations on how to generate
high(er)-fidelity synthetic data for specific tasks.
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