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    Home»Artificial Intelligence»The Definitive Guide to Structured Data Parsing with OpenAI GPT3.5 | by Marie Stephen Leo | Apr, 2024
    Artificial Intelligence

    The Definitive Guide to Structured Data Parsing with OpenAI GPT3.5 | by Marie Stephen Leo | Apr, 2024

    Jupiter NewsBy Jupiter NewsApril 16, 20242 Mins Read
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    Systematically evaluating Teacher, Fructose, and Langchain for 3 advanced real-world structured information parsing duties.

    Towards Data Science

    Picture generated by Creator utilizing ChatGPT

    Parsing structured information from Massive Language Fashions (LLMs) could be irritating for something past toy issues. But, reliably parsing LLM outputs into pre-defined constructions is essential to integrating LLMs into different software program programs and generative AI apps. OpenAI has taken the lead by releasing the GPT operate calling (Link) and JSON mode (Link). Nonetheless, these require intensive immediate engineering, sturdy parsing, retry, and swish error dealing with to work reliably for manufacturing real-world issues.

    Beneath are some issues I’ve confronted parsing structured information with LLMs. This text was written completely by a human with assist from Grammarly’s grammar checker, which has been my writing technique since 2019.

    1. Classification: The LLM should strictly adhere to an inventory of allowed courses, which could be as many as tens to tons of in real-world issues. LLMs begin hallucinating about disallowed courses in duties with greater than a handful of courses.
    2. Named Entity Recognition (NER): The LLM ought to solely decide entities explicitly current within the textual content. These entities may be in a 2- or 3-level deeply nested construction like Person → Tackle → Metropolis. LLMs wrestle to reliably establish these deeply nested fields and both miss them or hallucinate one thing that doesn’t exist.
    3. Artificial Information Technology: Just like NER, you may require a 2- or 3-level deeply nested information construction, so the challenges are the identical.

    Fortunately, some open-source tasks goal to resolve these challenges, however I’ve been getting combined outcomes from them on advanced real-world issues like these talked about above. So, I got down to systematically evaluate the three open-source frameworks that I’ve used: Teacher (Link), Fructose (Link), and everybody’s favourite Langchain (Link), to establish the perfect general framework for the above three duties on more difficult real-world eventualities. Spoiler alert: it’s Fructose!

    • Check out-of-the-box efficiency with…



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    The Definitive Guide to Structured Data Parsing with OpenAI GPT3.5 | by Marie Stephen Leo | Apr, 2024

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