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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