An LLM Maturity Model for Reliable and Transparent Text-to-Query
This addresses reliability and transparency problems for users in text-to-query applications, particularly in law enforcement, but is incremental as it builds on existing LLM evaluation frameworks.
The paper tackles reliability and transparency issues in Large Language Models (LLMs) for text-to-query applications by proposing a maturity model that evaluates beyond correctness, and demonstrates it with QueryIQ, a domain-specific assistant for law enforcement to expedite workflows and reveal hidden data relationships.
Recognizing the imperative to address the reliability and transparency issues of Large Language Models (LLM), this work proposes an LLM maturity model tailored for text-to-query applications. This maturity model seeks to fill the existing void in evaluating LLMs in such applications by incorporating dimensions beyond mere correctness or accuracy. Moreover, this work introduces a real-world use case from the law enforcement domain and showcases QueryIQ, an LLM-powered, domain-specific text-to-query assistant to expedite user workflows and reveal hidden relationship in data.