This is very cool. Had never considered the complexities of building a multi tone bot.
I recommended a straight line from the stated requirement to something that probably works. I like to start simple.
Originally the concept comes from our research on enforcing the brand voice in generated texts. The “no style” is used as an inert buffer between the source text and the brand styled text. This way we reduce the source style influence on the final text.
As a result you can use what ever input (from a standard chat bot or writing assistant) and style it to your brand with a simple “conversion” extra step. Way cheaper and simpler on a long run than complicated fine tuning to a tone. And voices become like skins you put on the text before the output…
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Smart. Very helpful. It’s similar to separating content from presentation rendering. In doing so, your content becomes more agile and over time, you can repurpose at a far lower cost.
Straight-line implementations tend to be tactical, while strategic engineering will pay huge dividends.
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Yes… Another benefit, you can use similar approach to shorten the text, not to the bullet points, but rather a thought / logic flow, stripping all unnecessary noise from it. Then you can reconstruct it back to any style with any degree of “decoration” you want by adding one or two simple “decompress” steps. And those texts in most cases pass the AI detection tools (at least the ones I tested).
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Great idea, never even considered something like that myself.
Thanks for sharing!
You welcome. Feel free to ask if you have some “complicated” questions.
Not at all.
If I understand correctly, I could see a huge market of this kind of data for people to spin with their personality to create unique content
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But there are not really two people. @chirag.shah285 wants to build a chatbot so they just want the chatbot to use their tone.
Your technique is extremely cool and useful but perhaps overkill for this use-case?
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Thank you @sergeliatko for your thoughts on creating a model with a certain “personality”. I was struggling on how to do this, and the main reason was that I was puzzled is that I went about it wrong. My basic thought was training it on a back and forth “interview” of data. But there, I am not training tone or personality, I am training a poor Q/A bot. And poor because Q/A really needs to use embeddings.
So with your personality training strategy by using a neutralizer on the original text, and create a fine-tune on the inverse of this is what I was missing. This makes sense conceptually since your fine-tune only is trained on the tone and personality.
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Thanks. Maybe you’re right. My reasoning for this is following:
1 the cost and effort to implement this “tone skin” possibility is relatively cheap and easy
2 the feature in itself allows a fair abstraction between the output content and it’s form, leaving you the possibility to “switch” brand tone later on without modifying the core bot functionality/training.
3 the two points above contribute to the ability to sell the business (and the bot) easier to a different brand that can change the tone of they need…
For several hundreds of dollars ( les than 100 for training, + extra cost of conversions in use) this adds tens of thousands of dollars to the value of the business.
As for “person” vs “bot” personality, maybe the term I used is not the most precise, but you get the idea.
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I wrote about this recently.
Creating a style guide to use in GPT Prompts
If you give it a reasonably sized sample - I just used one article - then it can generate a “style guide” that you can use in subsequent new prompts.
You can tweak the style guide as you see fit.
Stephen