  NL2VIS (natural language to visualization) is a promising and recent research
area that involves interpreting natural language queries and translating them
into visualizations that accurately represent the underlying data. As we
navigate the era of big data, NL2VIS holds considerable application potential
since it greatly facilitates data exploration by non-expert users. Following
the increasingly widespread usage of generative AI in NL2VIS applications, in
this paper we present V-RECS, the first LLM-based Visual Recommender augmented
with explanations(E), captioning(C), and suggestions(S) for further data
exploration. V-RECS' visualization narratives facilitate both response
verification and data exploration by non-expert users. Furthermore, our
proposed solution mitigates computational, controllability, and cost issues
associated with using powerful LLMs by leveraging a methodology to effectively
fine-tune small models. To generate insightful visualization narratives, we use
Chain-of-Thoughts (CoT), a prompt engineering technique to help LLM identify
and generate the logical steps to produce a correct answer. Since CoT is
reported to perform poorly with small LLMs, we adopted a strategy in which a
large LLM (GPT-4), acting as a Teacher, generates CoT-based instructions to
fine-tune a small model, Llama-2-7B, which plays the role of a Student.
Extensive experiments-based on a framework for the quantitative evaluation of
AI-based visualizations and on manual assessment by a group of
participants-show that V-RECS achieves performance scores comparable to GPT-4,
at a much lower cost. The efficacy of the V-RECS teacher-student paradigm is
also demonstrated by the fact that the un-tuned Llama fails to perform the task
in the vast majority of test cases. We release V-RECS for the visualization
community to assist visualization designers throughout the entire visualization
generation process.
