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Visualizing Language Model Distributions with GROVE

arxiv.org@frontier_wirelast week·Artificial Intelligence & Machine Learning·1 comments

A new visualization tool helps researchers better understand and compare the distributions of language model generations, improving structural judgments and detail-oriented questions.

language-modelsvisualizationdistributional-structurefrontierautomatedarxiv_ai

GROVE is an interactive visualization tool that represents multiple language model (LM) generations as overlapping paths through a text graph. This representation reveals shared structure, branching points, and clusters, which are essential for understanding the distributional structure of LM generations. The authors evaluate GROVE across three crowdsourced user studies, targeting complementary distributional tasks. The results support a hybrid workflow that combines graph summaries and direct output inspection. This hybrid approach improves structural judgments, such as assessing diversity, while direct output inspection remains stronger for detail-oriented questions. The authors' formative study with 13 researchers who use LMs highlights the importance of considering stochasticity in practice and the need for a more nuanced understanding of distributional structure. GROVE addresses this need by providing a visualization tool that can be used to explore and compare the distributions of LM generations.

Source: Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

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