21–30 of 69 posts
See how it's applied to Japanese learning here: https://elldev.com/feed/grsly
On the scheduling end, I'm surprised the article didn't mention https://github.com/fasiha/ebisu which uses Bayesian statistics. When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random. Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more…
Earlier quoted context omitted.
I've been playing with something similar, but far less thought out than what you have. I have a script for it, but am basically waiting until I can run a powerful enough LLM locally to chug through it with good results. Basically like the knowledge tree you mention towards the end, but attempt to create a knowledge DAG by asking a LLM "does card (A) imply knowledge of card (B) or vice versa". Then, take that DAG and…
I'd love to see it. What I like about your approach is that it circumvents the data problem. You don't need a dataset with review histories and flashcard content in order to train a model.
GPT-4 can probably estimate whether two flashcards are functionally equivalent
https://notes.andymatuschak.org/zJ7PMGzjcgBUoPjLUHBF9jn
GPT-4 can probably estimate whether one prompt will spoil retrieval of another
> [....] Ignoring the following factors means we are leaving useful information on the table: > 1. The review histories of related cards. Card semantics allow us to identify related cards. This enables memory models to account for the review histories of all relevant cards when estimating a specific card’s retrievability. > 2. [...] I've been thinking that card semantics shouldn't be analyzed at all, and just treated…
I’ve incorporated many different things into the SRS, from vector embeddings to graph based association to lemma clustering to morpheme linking, and was surprised how much of these I took out.
Most of the unlocks with the SRS have been more in application space. Doing reviews with Anki feels like a chore, and I’m always counting down the reviews left to do. Reviews with Phrasing however are much more addictive, and I routinely spent an extra 30+ minutes in that “ok just one more card” loop.
We will never be able to know with 100% certainty how well you know a card, but FSRS gets us darn close. I think the interesting stuff is less about improving that metric, and more about what can you do with that information.
Thanks to the whole FSRS team btw (I assume y’all will be reading this hn post) And if anyone is curious I wrote up a bit about my SRS here: https://phrasing.app/blog/humane-srs
I wonder if the author has ever considered reaching out to makers of Anki decks used by premeds and medical students like the AnKing [1]. They create Anki decks for users studying the MCAT and various Med School curricula, so have a) relatively stable deck content (which is very well annotated and contains lots of key words that would make semantic grouping quite easy) b) probably contains loads of statistics on user reviews (since they have an Anki addon that sends telemetry to their team to make the decks better IIRC), and c) contains incredibly disparate information (all the way from high-school physics to neurochemistry).
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On the scheduling end, I'm surprised the article didn't mention https://github.com/fasiha/ebisu which uses Bayesian statistics. When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random. Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more…
> When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random. >Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour. Just the other day I was thinking about how there’s a good chunk of vocab that could be “min…
It has Anki integration or its own SM2 flashcards app (soon FSRS). And it passively collects a personal corpus of sentences from any web/ebook material you open (manga up next).
I plan to add more sophisticated sync between the reading and reviewing such that cards can be more dynamically based on relevant personal corpus content, and where reading (on the web or in books, outside flashcards) would auto-review any flashcards you have (or which you create in the future).
There really shouldn’t be any difference between encountering a word in something you’re reading and reviewing it on a flashcard. And it would be nice to revisit reading material with guidance from FSRS, to find N+1 sentences for learning new words and to find excerpts containing words that are due for review.
I’ve been working on https://phrasing.app for a while now, including many iterations of the SRS. It’s been my experience that most of these sorts of improvements are really imperceptible. While I use FSRS as a base, and I’m very happy with the results it provides, it’s really only a few percentage points off of the SM-2 algorithm from the 90s. It’s slightly less stressful, definitely more accurate, but I think only a…
I’ve been working on https://phrasing.app for a while now, including many iterations of the SRS. It’s been my experience that most of these sorts of improvements are really imperceptible. While I use FSRS as a base, and I’m very happy with the results it provides, it’s really only a few percentage points off of the SM-2 algorithm from the 90s. It’s slightly less stressful, definitely more accurate, but I think only a…
Can't help but repeat this old joke: A guy bought a gym-membership for 6 months, and paid $1000. But he was lazy (like most of us are) and never went or very rarely went to the gym, never felt like going there. After 6 months he realized he had wasted $1000. So he thought maybe if he bought the equipment himself he could and would do exercise at home. He bought the equipment for $1000, but then he rarelywent home. Didn't feel like it :-)
I’ve been working on https://phrasing.app for a while now, including many iterations of the SRS. It’s been my experience that most of these sorts of improvements are really imperceptible. While I use FSRS as a base, and I’m very happy with the results it provides, it’s really only a few percentage points off of the SM-2 algorithm from the 90s. It’s slightly less stressful, definitely more accurate, but I think only a…
Would you minding writing an intro for your app if it is using FSRS? https://github.com/open-spaced-repetition/awesome-fsrs
Seriously, thank you for everything you've done. You've created something truly great :)