Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models

Understand

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.