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.

NL2VIS 研究领域涉及将自然语言查询解释并将其转化为准确代表底层数据的可视化，为非专家用户大大便利了数据探索；本文提出了 V-RECS，首个具有解释、标题和进一步数据探索建议的基于 LLM 的视觉推荐系统，通过细化小型模型的方法解决了强大 LLMs 的计算、可控性和成本问题，使用 Chain-of-Thoughts（CoT）技术来生成有洞察力的可视化叙述来辅助 LLM 的指导，V-RECS 在性能上与 GPT-4 相当，但成本更低，该研究通过发布 V-RECS 帮助可视化设计师整个可视化生成过程。