English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Large Language Models (LLMs) have democratized synthetic data generation,which in turn has the potential to simplify and broaden a wide gamut of NLPtasks. Here, we tackle a pervasive problem in synthetic data generation: itsgenerative distribution often differs from the distribution of real-world dataresearchers care about (in other words, it is unfaithful). In a case study onsarcasm detection, we study three strategies to increase the faithfulness ofsynthetic data: grounding, filtering, and taxonomy-based generation. Weevaluate these strategies using the performance of classifiers trained withgenerated synthetic data on real-world data. While all three strategies improvethe performance of classifiers, we find that grounding works best for the taskat hand. As synthetic data generation plays an ever-increasing role in NLPresearch, we expect this work to be a stepping stone in improving its utility.We conclude this paper with some recommendations on how to generatehigh(er)-fidelity synthetic data for specific tasks.

Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science