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It's definitely gotten better, but yeah, it really doesn't reliably support what I'm currently working on. My project takes transcripts from YouTube, which don

It's definitely gotten better, but yeah, it really doesn't reliably support what I'm currently working on. My project takes transcripts from YouTube, which don't have punctuation, splits them up into chunks, and passes each chunk to GPT-4 telling it to add punctuation with paragraphs. Part of the instructions includes telling the model that, if the final sentence of the chunk appears incomplete, to just try to complete it. Anyway, GPT-3.5-turbo works okay for several chunks but almost invariably hits a case where it either writes a bunch of nonsense or spells out the individual letters of words. I'm sure that there's a programmatic way I can work around this issue, but GPT-4 performs the same job flawlessly.

minimaxir 3y ago
Semi off-topic but that's a use case where the new structured data I/O would perform extremely well. I may have to expedite my blog post on it.
selalipop 3y ago
If GPT 4 is working for you I wouldn't necessarily bother with this, but this is a great example of where you can sometimes take advantage of how much cheaper 3.5 is to burn some tokens and get a better output. For example I'd try asking it for something like : { "isIncomplete": [true if the chunk seems incomplete] "completion": [the additional text to add to the end, or undefined otherwise] "finalOutputWithCompletion": [punctuated text with completion if isIncomplete==true] } Technically you're burning a ton of tokens having it state the completion twice, but GPT 3.5 is fast/cheap enough that it doesn't matter as long as 'finalOutputWithCompletion' is good. You can probably add some extra fields to get an even nicer output than 4 would allow cost-wise and time-wise by expanding that JSON object with extra information that you'd ideally input like tone/subject.
popinman322 3y ago
I've done exactly this for another project. I'd recommend grabbing an open source model and fine-tuning on some augmented data in your domain. For example: I grabbed tech blog posts, turned each post into a collection of phonemes, reconstructed the phonemes into words, added filler words, and removed punctuation+capitalization.
- swores 3y ago
  Sounds interesting, any chance you could share either your end result that you used to then fine-tune with, or even better the exact steps (ie technically how you did each step you already mentioned)? And what open LLM you used it with / how successful you've found it?

minimaxir 3y ago

Semi off-topic but that's a use case where the new structured data I/O would perform extremely well. I may have to expedite my blog post on it.

selalipop 3y ago

If GPT 4 is working for you I wouldn't necessarily bother with this, but this is a great example of where you can sometimes take advantage of how much cheaper 3.5 is to burn some tokens and get a better output. For example I'd try asking it for something like : { "isIncomplete": [true if the chunk seems incomplete] "completion": [the additional text to add to the end, or undefined otherwise] "finalOutputWithCompletion": [punctuated text with completion if isIncomplete==true] } Technically you're burning a ton of tokens having it state the completion twice, but GPT 3.5 is fast/cheap enough that it doesn't matter as long as 'finalOutputWithCompletion' is good. You can probably add some extra fields to get an even nicer output than 4 would allow cost-wise and time-wise by expanding that JSON object with extra information that you'd ideally input like tone/subject.

popinman322 3y ago

I've done exactly this for another project. I'd recommend grabbing an open source model and fine-tuning on some augmented data in your domain. For example: I grabbed tech blog posts, turned each post into a collection of phonemes, reconstructed the phonemes into words, added filler words, and removed punctuation+capitalization.

swores 3y ago
Sounds interesting, any chance you could share either your end result that you used to then fine-tune with, or even better the exact steps (ie technically how you did each step you already mentioned)? And what open LLM you used it with / how successful you've found it?