Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models

Emily Reif, Minsuk Kahng, Savvas Petridis

Room: 104

2023-10-26T22:36:00ZGMT-0600Change your timezone on the schedule page
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LinguisticLens, a new visualization tool for making sense of text datasets synthesized by large language models (LLMs) and analyzing the diversity of examples.
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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 interactive 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