Summary of An Llm Maturity Model For Reliable and Transparent Text-to-query, by Lei Yu (expression) and Abir Ray (expression)
An LLM Maturity Model for Reliable and Transparent Text-to-Query
by Lei Yu, Abir Ray
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a Large Language Model (LLM) maturity model tailored for text-to-query applications to address reliability and transparency issues. The model goes beyond mere correctness or accuracy by incorporating additional dimensions. A real-world use case from law enforcement is showcased, featuring QueryIQ, an LLM-powered assistant that accelerates user workflows and uncovers hidden relationships in data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix problems with very smart computers (Large Language Models) that answer questions. It creates a special way to measure how good these computers are at helping people find answers they need. The researchers show how this can be used in real life, like in law enforcement, to make it easier and more helpful for people to get the information they want. |
Keywords
» Artificial intelligence » Large language model
