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I've just read this paper yesterday https://arxiv.org/abs/2303.06135
Chai is a relatively small team that seem to solve this problem
Cool paper, what do you mean by "Solve".
I don't see datasets on their page, which is why I ask. https://huggingface.co/ChaiML
Apologies, I was on the go and just decided to put a quick comment with link to the paper.
By "solve" I really mean that they seem to have found ways to deal with the third step:
>3. Fine-tune the model further with RLHF to match user values and interests. This is the fine-tuning step I refer to above, extremely new to NLP.
And yes, they don't open source anything unfortunately, I missed this point.
Wow, this article provides some intriguing insights into the challenges faced by open-source communities when it comes to training large language models. The concept of "moats" protecting companies through data and habits is quite fascinating. The author does a great job of explaining the data-intensive process involved in training chatbots based on these models, emphasizing the costly nature of fine-tuning and the need for new datasets and engineering infrastructures. It's clear that open-source efforts have a tough battle ahead. To delve deeper into the world of AI and its impact on dating, I recently stumbled upon a site called LoveBoost-AI. It utilizes the power of Generative AI to enhance your dating profile pictures, presenting the best version of yourself. If you're curious about leveraging AI for your dating apps, check out LoveBoost-AI at http://dating.tiktak-studio.com. It's an exciting application of this technology!