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As much as I hate LLMs... do LLMs use Chinese Restaurant Processes and/or Kneser-Ney?

Perfect! Thank you so much.

Thanks, that was informative. Learned something.

I haven't finished the video, so apologies if you cover it, but in the 2023 CS224N NLP lecture on coreference resolution, Chris Manning introduces the (very complicated and demoralizing, to me) Hobb's algorithm, and then basically says something like "Hobbs HIMSELF said publicly that he didn't like the algorithm, and often pointed to it as ~an example of how we clearly needed something better."

Synthetic data generations has become so easy now thanks to LLM!

Your video has sparked a meaningful conversation. How has being a young-onset Parkinson's patient shaped Jessica's perspective on life? As the host of a dream interpretation channel, I'm curious to explore how her experiences with Parkinson's influence her dreams and subconscious mind. I truly appreciate the opportunity to learn more about Jessica's journey, and I've already liked and subscribed to the channel for more insightful content like this.

13:11 there is mistake in last line t(e1,f2) * ( t(e2,f0) + t(e2,f1) + t(e2,f2) ) should be this slides duplicate f2

Jordan is soooooooo cool ...🤭 I really want to meet you at the NLP conference ...!!!!

Can you share this video with the president of Harvard? I don’t think she got the message. Yet somehow DEI still think it was okay for her to cheat. DEI is accusing everyone of racism.

Awesome, can't believe guys tried doing this in your class. This is like commiting a burglary and leaving a confession note and a business card. This is really funny.

thanks for using my slides! :)

My book mentioned about continuous and discrete data, but they did not explain anything. Your video clarified it so well for me Thanks a lot!!!

Fire video after fire video with this guy. Incredible.

Great intro video, and lovely coverage of the key concepts there. I listened to the guy credited with coming up with the transformer model, and I think in adjusting the word vectors to predict the next word in a sequence more effectively, its also mapping phrases, sentences, ideas and concepts into multidimensional space, up to its input context length. So it ends up having what Isaac Asimov described as a "perceptual schematic" of the world, how everything relates to everything else, encoded in multidimensional space. Then all the behaviours it's trained to perform based on rlhf are possible because it has this initial perceptual schematic.

This is an amazing video. Very intuitive. Thank you.

Incredible work. Sharing with my class.

This was very helpful tyvm!

9:42 for Rm(H), what is the use of taking expectation over all samples? As we saw previously, like from 6:12, calculation of empirical Rademacher does not use the true label of samples, rather just the size of the sample.

Interesting

It's really wonderful when no nonsense science communication comes with a generous helping of low-key courage. Dope.

Really excellent like the other videos on this series. I am sharing the course with colleagues and hoping to go thru the syllabus in the Spring. Thank you for the excellent work, Prof!

Excellent

This is incredible. Thanks!

in 6:55, it is said that H(x, M) = sum(log(M(xi))), but accroading to the defination of cross entropy, it should be H(P, Q) = sum(-1 *P(x)log(Q(x))), so are we assuming P(x) is always one when computing perplexity?

Excellent interview. Excellent YT Channel, thank you!

That's just a brilliant video, I appreciate the fact that your videos always introduce an uncommon point of view that still makes a lot of sense.

IS there a link to the "mark riddle(?)" transformer diagram? can't find it in the description.

Amazing video!! I’m so glad you covered this. Your passion and enjoyment about the subject really comes through. Thanks so much for this ☺️

should equation 6) be: 2e^(-epsilon*m/2)? This is because the chance of sampling from the whole highlighted region is epsilon, so the probability of sampling from a specific region is epsilon/2? Thank you for the great lecture!

Cool idea! Other rating ideas: how evenly does the straight line cut the country into two pieces? Are they the same size? Same Population each side of the line? This way you can allow for easy countries and hard countries, where you can score the "even" disection of countries irrespective of how long the line is. Also a hint: your microphone has some awful automatic gain setting or something, where all the quiet sounds are amplified and all the loud sounds are quieted down, so your tiniest breathing in is the same volume as your loudest talking bits. It's really annoying

Loved this video 😅 for some reason ❤

the name muppet models is super cute but alas the perspective that muppet models just make stuff up is misplaced, true in some ways but also dangerously wrong, they do get things wrong or out of place when speaking off the top of their head, but, um, statistically far less than humans do already, the confusion is that they're so much better at talking than humans that they can give almost accurate coherent essays about stuff completely off the top of their heads while a human would just be saying "uhhhhh", if you give them the equivalent of a human salary worth of compute they can also check the accuracy of things a zillion times better than any human could ever check

Dance your cares away <clap><clap> Worries for another day <clap><clap>

It's canon now.

You don't have enough views.

Yuval Pinter makes the excellent point that I shouldn't conflate "writing system" and "language". Indeed, this video should have been titled "How to Know if Your Writing System is Broken". See more in their excellent position paper on the subject: aclanthology.org/2023.cawl-1.1/

Lol what was that short powermark on question 32

Interesting way of presenting while teaching, never seen this method before!

Loved the lecture

Stop teleporting!!!

Content starts at 3:13

Thank you master

Splendid explanation!

I need to do Google reviews classification into n categories and will this model give me 💯 accurate results..i am worried about the accuracy 😮

what if them calibration error is not uniform across all topics? then we cannot use generalized bins since sometimes the model would output the correct accuracy estimate right?

Thank you for the video, would you be able to share a link to Mark Riedl paper showing the detailed architecture of transformers?

Hey Jordan, there’s a product called gptzero which says it can detect whether a certain piece of text is generated by an LLM like ChatGPT or not, they do that by outputting a perplexity score for the text you provide and assume that it’s most likely LLM-generated if it has a low PPL. My question: how can they calculate the PPL score if they do not have the actual model that can give them the entropy loss?

Super clear!😀😀

You are great teacher! Love your videos. And I am so happy that you made all the course available online!

Hello , may I ask that if I will study computational linguistics I will find good chances of jobs and good salary or better to take general linguistics