title: Visualizing Linguistic Diversity of Text Datasets Synthesized by Large
    Language Models
summary: Large language models (LLMs) can be used to generate smaller, more refined
  datasets via few-shot prompting for benchmarking, fine-tuning or other use
  cases. However, understanding and evaluating these datasets is difficult, and
  the failure modes of LLM-generated data are still not well understood.
  Specifically, the data can be repetitive in surprising ways, not only
  semantically but also syntactically and lexically. We present LinguisticLens, a
  novel inter-active visualization tool for making sense of and analyzing
  syntactic diversity of LLM-generated datasets. LinguisticLens clusters text
  along syntactic, lexical, and semantic axes. It supports hierarchical
  visualization of a text dataset, allowing users to quickly scan for an overview
  and inspect individual examples. The live demo is available at
  shorturl.at/zHOUV.
paper_authors._id: 646acd1835c7a57f936c45fb
paper_authors.name: Emily Reif
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-05-22T15:24:35.303Z
paper_authors._id: 646b88fb942fd7c135dc0ec1
paper_authors.avatarUrl: /avatars/0ebcb6cc72458b947f034ac8e13611e8.svg
paper_authors.fullname: Emily Reif
paper_authors.type: user
paper_authors.user: emilyreif
paper_authors._id: 646acd1835c7a57f936c45fc
paper_authors.name: Minsuk Kahng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-22T12:47:56.824Z
paper_authors._id: 6301f3fb4ccccaa23d38fb7f
paper_authors.avatarUrl: /avatars/4226111991d1788d3d442ffb2f33ea48.svg
paper_authors.fullname: Minsuk Kahng
paper_authors.type: user
paper_authors.user: minsukkahng
paper_authors._id: 646acd1835c7a57f936c45fd
paper_authors.name: Savvas Petridis
paper_title: Visualizing Linguistic Diversity of Text Datasets Synthesized by Large
    Language Models
paper_summary: Large language models (LLMs) can be used to generate smaller, more refined
  datasets via few-shot prompting for benchmarking, fine-tuning or other use
  cases. However, understanding and evaluating these datasets is difficult, and
  the failure modes of LLM-generated data are still not well understood.
  Specifically, the data can be repetitive in surprising ways, not only
  semantically but also syntactically and lexically. We present LinguisticLens, a
  novel inter-active visualization tool for making sense of and analyzing
  syntactic diversity of LLM-generated datasets. LinguisticLens clusters text
  along syntactic, lexical, and semantic axes. It supports hierarchical
  visualization of a text dataset, allowing users to quickly scan for an overview
  and inspect individual examples. The live demo is available at
  shorturl.at/zHOUV.
paper_ai_summary: LinguisticLens is an interactive tool for visualizing and analyzing syntactic diversity in datasets generated by large language models.
paper_ai_keywords: large language models few-shot prompting benchmarking fine-tuning LinguisticLens syntactic diversity lexical diversity semantic diversity hierarchical visualization