An LLM Maturity Model for Reliable and Transparent Text-to-Query
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917595995701248 |
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| author | Yu, Lei Ray, Abir |
| author_facet | Yu, Lei Ray, Abir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An LLM Maturity Model for Reliable and Transparent Text-to-Query Yu, Lei Ray, Abir Computation and Language Artificial Intelligence 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. |
| title | An LLM Maturity Model for Reliable and Transparent Text-to-Query |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.14855 |