- An LLM can be represented by a model which has two components, an interface, and a computing function
- Both components are separate yet interoperable
- The computing function performs the core tasks performed by the model (e.g. generating a software code)
- The interface part interprets human language queries to the computing function (e.g. providing user requirements for a software code)
- The interface part translates the output of the computing function into an easily understandable format.
- The purpose of the whole system is to align the request at its input with the response at its output
- Hallucination occurs when the output of the computing function is inaccurate yet the interface part translation of this output still makes sense
- An output response of the system due to a prompt provided at its input is generated through an interaction between both the interface and computing function components
- Fine tuning of the system using small datasets creates a new model which includes its own interface and computing function components
Pavan Kumar Gondhi
I agree with Peter's points above and while your points capture the idea of processing input and generating output, they do not fully reflect the integrated nature of LLMs like GPT-3. The model's neural network handles interpretation and generation in a unified manner, without distinct interface and computing components. Hallucinations result from the inherent limitations of the model rather than a separate interface component. Fine-tuning refines the existing model rather than creating new separate components. Hope this helps Mohamed el Nawawy !
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