| Название | : | [New Paper] Teach LLMs Domain Knowledge |
| Продолжительность | : | 10.28 |
| Дата публикации | : | |
| Просмотров | : | 636 |
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Three problems about this article:br1 It didn't say what is "fine-tuning" in Table 1 br2 It didn't compare Fine-tuning vs their method AdaptLLMbr3 LLaMA is an aligned model, you cannot just simply post train on it and claim that the prompting ability is hurt Comment from : rand |
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Now a freaking cool word that you used at 0:37, "imbibing" Completely new to me, and I don't think I've ever heard it before, but I love it and am glad to say that I know it now thanks to you Comment from : What’sUp |
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Thank you brother for making aware of such great papers Please keep inspiring us Thank you 😊💕 Comment from : Future Being |
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Excellent detailed information thanks Comment from : Vaibhav Patil |
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What are parameters? Please explain it Comment from : Ramasamy Nagappan |
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Love these LLMSs updates! One thing I dont quite understand though is that using a pretrained model how much data is needed for further fine-tuning for it to work effectively? Comment from : Jokke |
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i love the new playlist of (New Paper), you are saving a lot of time while struggling to read the paper for the first time, after watching you video, reading it is so simple bryou saved me a lot of time bro Comment from : Mohamed Hatem |
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Good evening sir Comment from : Dave B |
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Training LLM in the understanding and output generation of "Domain iSpecific/i Knowledge" is what we have been concentrating on for the past 6 Months I actually read this particular paper some time ago, and my conclusion is they are attempting to weight performance by comparing small(er) foundational frameworks trained using the DAPT methodology, against large(er) frameworks using the fine tuning approach We find, in using Falcon-40B, the performance is more than acceptable when fine tuned using domain iSpecific/i internal datasetsbrbrThanks for the video! Good job Comment from : shotelco |
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1st comment 1st view Comment from : Devasheesh Mishra |
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