I think that such fine-tuning hinges on what do you consider to be a mistake, which is context dependent. Having task-specific finetuned models goes against the status quo of generalization/centralization where few large companies serve a limited amount of models efficiently - both due to inference efficiency and the need/want to control the model.
Having a human-like LLM ecosystem with deep specialization requires a paradigm change in how LLMs are trained - and held accountable. How do we put trust in a specific finetuned LLM rather than the institution behind it? Is there any better approach than the very inefficient evolutionary?
Having a human-like LLM ecosystem with deep specialization requires a paradigm change in how LLMs are trained - and held accountable. How do we put trust in a specific finetuned LLM rather than the institution behind it? Is there any better approach than the very inefficient evolutionary?