Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations
This addresses the need for standardized evaluation in generative AI-based visualizations, though it is incremental as it builds on existing benchmarking initiatives for LLMs.
The paper tackles the problem of evaluating LLM-generated visualizations by proposing a theoretical evaluation stack, EvaLLM, which decomposes evaluation into atomic components and provides implementation guidance, and demonstrates its benefits through case studies on GPT-3.5-turbo and Llama2-70-b models.
The automatic generation of visualizations is an old task that, through the years, has shown more and more interest from the research and practitioner communities. Recently, large language models (LLM) have become an interesting option for supporting generative tasks related to visualization, demonstrating initial promising results. At the same time, several pitfalls, like the multiple ways of instructing an LLM to generate the desired result, the different perspectives leading the generation (code-based, image-based, grammar-based), and the presence of hallucinations even for the visualization generation task, make their usage less affordable than expected. Following similar initiatives for benchmarking LLMs, this paper copes with the problem of modeling the evaluation of a generated visualization through an LLM. We propose a theoretical evaluation stack, EvaLLM, that decomposes the evaluation effort in its atomic components, characterizes their nature, and provides an overview of how to implement and interpret them. We also designed and implemented an evaluation platform that provides a benchmarking resource for the visualization generation task. The platform supports automatic and manual scoring conducted by multiple assessors to support a fine-grained and semantic evaluation based on the EvaLLM stack. Two case studies on GPT3.5-turbo with Code Interpreter and Llama2-70-b models show the benefits of EvaLLM and illustrate interesting results on the current state-of-the-art LLM-generated visualizations.