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Show HN: Turning books into chatbots with GPT-3

konjer.xyz

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Re: Show HN: Turning books into chatbots with GPT-3

#162

Post by graiz »

Is this a general prompt on top of GPT or is this a fine tuned model? Since all these books are well known and popular it could be a GPT prompt... "I want you to act as if you're a book that knows it's own contents..." or it could be a fine-tuned model. The tuned model would be more interesting as you could feed it new data/books and material.

Re: Show HN: Turning books into chatbots with GPT-3

#165

Post by NicoleJO »

93po wrote: post #140
NicoleJO wrote: post #90

Earlier quoted context omitted.

Both of those books are copyrighted. Why are you illegally sharing them?

If you can manipulate the model into presenting you with an amount of copyrighted text that would count as infringement, it's clearly not the intention of the OP. In my attempts it shows, at most, a couple paragraphs. A quick Google search shows countless results that show a lot more. There is no way to meaningful infringe on the copyright with this tool. Not sure why you're being rude and unproductive with this comm…

Copyrighted content is not eligible for non-authorized reproduction.

Are people here so used to pirating that they no longer know what the laws are???

Re: Show HN: Turning books into chatbots with GPT-3

#167

Post by leobg »

d4rkp4ttern wrote: post #155
leobg wrote: post #30

Earlier quoted context omitted.

You don’t. You cut it into snippets. For those you create embeddings which allow you to rank them by semantic similarity to a query. You then prompt GPT3 with the question plus, say, the three most relevant snippets from the book. The most difficult thing about the process is preventing the model from making stuff up.

How do you create the embeddings? Is there a GPT3 API that returns a paragraph’s embedding vector?

There is. But I’m not sure how good they are. Nils Reimer wrote a Medium post about them, concluding that they performed worse than SOTA sentence transformer models.

Re: Show HN: Turning books into chatbots with GPT-3

#169

Post by leobg »

sputknick wrote: post #80
kreas wrote: post #48

Earlier quoted context omitted.

This is exactly what I'm working on! My project is taking Zoom conversation, using pyannote for speaker diarisation, whisper for transcription, pinecone.io for semantic search, then feeding that into GPT-3 so we can ask questions about conversation. For us this is super useful because it's not unusual for our discover sessions to last days and we're all terrible at taking notes. As a nerd, my brain is already buzzing…

Are you getting good results when summarizing a human speaking? On my project, even though Whisper does a good job translating it, I'm not happy with the query results. My theory is that GPT-3 is designed for written word and the way people speak and the way they write are structurally different. Or I'm just figuring this out and I'm not good enough at it yet.

It’s often not enough to just index the snippets themselves. You may need to augment them. For instance, you may need to keep track of the context, and prepend it to the actual snippet that you want to index.

The important thing in such a pipeline is not GPT 3. The important thing is the retrieving/ranking algorithm that finds the most relevant snippets and feeds them into GPT 3. The latter is only the mouthpiece, if you will.

In fact, you might even find that you’re better off without it (no confabulation, ground truth data).

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