Thanks for summarizing so many awesome papers. My best one was the knowledge distillation with student- teacher model. this is where SLM (Small Language Models) will have its strong use cases. They learn fast and run fast :)
I’m not very in the weeds with LLMs but this post was really helpful to learn about how researchers are thinking of making LLMs even more efficient. Excellent crash course, even for an LLM newbie like me!
It is such a pleasure reading you Kartik! Love how you broke down the concept of Recommendation System and give some modern application examples. Thanks for sharing!!
Fascinating post, Kartik! I'm interested to see how these LLM integrations will impact recommendation systems in practice as they will move towards wider implementation -- Particularly in how the probabilistic aspects might affect audience targeting accuracy and content personalization.
Awesome overview of the recommendation landscape, with lots of good links and rabbit holes to go down into. Keep up the great work!
Thanks @Ben. Means a lot coming from you !!
Thanks for summarizing so many awesome papers. My best one was the knowledge distillation with student- teacher model. this is where SLM (Small Language Models) will have its strong use cases. They learn fast and run fast :)
Thanks Nirmal for reading. Yeah for sure. Student teacher model is the way to go and gives great performance improvements !
I’m not very in the weeds with LLMs but this post was really helpful to learn about how researchers are thinking of making LLMs even more efficient. Excellent crash course, even for an LLM newbie like me!
Thank you Adriana ! Glad you liked it
It is such a pleasure reading you Kartik! Love how you broke down the concept of Recommendation System and give some modern application examples. Thanks for sharing!!
Thank you so much Josep !! Glad you liked it !!
Fascinating post, Kartik! I'm interested to see how these LLM integrations will impact recommendation systems in practice as they will move towards wider implementation -- Particularly in how the probabilistic aspects might affect audience targeting accuracy and content personalization.
Rohan yes!! Its very interesting times ahead. Actually they are impacting them even now but just under the hood in lot of the cases :)
Great insights into how LLMs can play a part in better recommendation engines. Thanks for sharing
Thanks for the feedback Hemant !
Excellent article! Thank you for including my article 😊
Thank you so much Logan !!
This was very insightful. It gives me a lot of inspiration for how I could use LLMs in future technical projects.
By the way, I’m quite interested in the topic of synthetic data generation. Have you played around with it?
Great article and thank you very much for the shoutout!
Yeah quite a lot actually. Happy to answer any questions !!
Great! I’ll send you a DM later 🙏