I can't find a post that I remember Google published just after all the ChatGPT SQL generation hype happened, but it felt like they were trying to counter that hype by explaining that most complex LLM-generated code snippets won't actually run or work, and that they were putting a code-evaluation step after the LLM for Bard.
(A bit like why did they never put an old fashioned rules-based grammar checker check stage in google translate results?)
That's interesting! Where it says that is will "learn iteratively from the results until it arrives at a final output" I assume it's therefore trying multiple LLM generations until it finds one that works, which I didn't know about before.
However, AFAIK it's only ever at inference time, an interpreter isn't included during LLM training? I wonder if it would be possible to fine tune a model for coding with an interpreter. Though if noone has done it yet there is presumably a good reason why not.
> Though if noone has done it yet there is presumably a good reason why not.
The field is vast, moving quickly and there are more directions to explore than researchers working at top AI labs. There's lots of open doors that haven't been explored yet but that doesn't mean it's not worth it, it's just not done yet.
I can't find a post that I remember Google published just after all the ChatGPT SQL generation hype happened, but it felt like they were trying to counter that hype by explaining that most complex LLM-generated code snippets won't actually run or work, and that they were putting a code-evaluation step after the LLM for Bard.
(A bit like why did they never put an old fashioned rules-based grammar checker check stage in google translate results?)
Fast forward to today and it seems it's a normal step for Gemini etc https://ai.google.dev/gemini-api/docs/code-execution?lang=py...