Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specifications

Abstract:What-if analysis (WIA) lets users explore hypothetical scenarios by adjusting parameters, applying constraints, and scoping data through interactive interfaces. Current tools fall short: spreadsheet and BI tools require laborious setup, while LLM-generated interfaces frequently misinterpret analytical intent. It remains unclear whether an explicit intermediate representation is needed, or whether LLMs can generate WIA interfaces directly. We build a benchmark of 405 WIA questions across 11 types and 5 datasets with human-authored ground-truth intent, and find that direct one-shot generation across three LLMs fails to produce a working interface in 17-45% of cases and, when interfaces render, often misrepresents the question's objective (up to 77.5%) and constraints (up to 58.2%). We therefore present a two-stage workflow that translates NL questions into Praxa Specification Language (PSL) specifications, then compiles them into interfaces. Because errors localize to named properties, taxonomy-guided repair raises correctness from 52.42% to 80.42%, unlike errors in directly generated code.

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2604.07652 [cs.AI]

(or arXiv:2604.07652v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.07652 Focus to learn more arXiv-issued DOI via DataCite

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