The source was from a company named DuXiu, or previously SuperStar. They collaborated with the libraries around China and scanned their collections since early 2000-ish. Before that I think they just bought some junk books from recycling stations based on the quality of early samples.

Many of the books are translated versions of the textbooks from the west (most likely the US) and many are pure political propaganda junk. Some literature and history stuff which were published when censorship wasn't so extreme.

Many of the Chinese tech companies should have access to this collection (especially Baidu for sure) but the books were not censored based on today's standards so I doubt any of them would openly use them not only due to the copyright issue but also the political risks.

The benefit of Anna’s Archive is that they have goodwill on their side. They are trying to contribute to academic research directly, whereas libgen seems to prioritize profit (and has to, to survive).

Anna’s Archive does that too. The "donations" are actually paying to access features. But by proactively reaching out to the academic community like this, they’ll make a lot of powerful allies.

EDIT: on second thought, how does libgen monetize? I thought they ran ads, but I just realized I didn’t verify that.

Whoever runs a library like this for altruistic reasons stands to win. The desire to get rich is most of the problem with doing shady things scalably. One can argue that it’s the risk/reward tradeoff, but I suspect as technology makes everything more accessible, an operation like this only costs a few thousand a month. That’s achievable while flying under the radar long enough to hopefully avoid getting in a lot of trouble.

Of course, as with death note, everything comes down to: don’t get caught.

The original LibGen, under anonymous and altruistic founder bookwarrior, did not aim to monetize but was built purely on volunteer effort. Some of the LibGen domains, however, are forks that put up advertisements.

What do we do now?

I just realized that western companies are screwed because of the people who wanted to make a buck by creacting artificial scarcity.

I have been predicting for decades that IP laws would bring about the end of our civilization. I did not know how it would happen. Now I know.

We are going back to the time where GDP was just how many farmers did your country have. But in 2000s it will be how many STEM people does your country have and how much IP one can create and license. tech IP is like a global rent from everybody that consumes tech.

Will people call this theft? No one has so far.

In the meantime, simply abandoning European and American knowledge is a realistic option. LLMs will learn in Chinese.

On the other hand, it would suck much more to be thrown in a Chinese prison. So probably don’t do this if you plan on visiting China. (If China reacts by fully endorsing this, that would be the uno reverse of the century.)

By explicitly supporting this, China could signal to AI researchers worldwide that China is a safe haven. It’s the materialization of what everyone said would happen: when copyright enthusiasts shut down AI work in America, companies will simply go elsewhere.

Imagine if the epicenter of AI research shifted away from the US. This may seem unlikely, but lack of data is the biggest problem that open source LLMs face. Mistral had to completely refuse to disclose their training sources just to release a decent model.

Personally, I’m tempted to focus on this full time. It’s what I’ve always wanted to do. I never gave a thought to legal consequences, and with this collection there appears to be none.

Heh. It’s a safe haven until your LLM says something the CCP doesn’t like.

The idea that overly burdensome regulations around open source models in the US shifts the global center of mass of LLMs to China is not implausible.

One reason resonant among government officials against opensource LLMs is to keep them from China and Russia. But among the very best embeddings, ~13Bs and perhaps 34B open-weights models are the Chinese ones. The recent DeepSeekCoder also tops EvalPlus: https://evalplus.github.io/leaderboard.html For those concerned about over-training, ChatGPT and GPT4 are almost certainly also contaminated and there have been independent confirmations of DeepSeekCoder's strength.

Especially due to no freedom of the press in China (you need a permit for each published book) they are all already censored.

I still don't think China could be a safe haven though, they haven't shot down open source models not because they are nice, it's just that they are incompetent.

Even in a fully censored environment, you can still accidentally run afoul of state censors.

https://www.theguardian.com/world/2022/jun/06/chinese-star-t...

Sort of joking, but some people seem to be using the dual LLM approach where the 2nd LLM censors the output of the first. Take the Gandalf game as an example.

And while you don't need an LLM to censor an entire internet at scale I see how it would be a useful tool for the censors in China or anywhere else. "Hey CensorGPT does the following weibo post history make fun of dear leader, taking note of the themes of Winnie the Pooh, touch on any other criticism of the CPC" etc

I’m not a fan of bowing to censorship (to say the least), but at this point I am even less a fan of being verbally spit on, called a thief, and being included in lawsuits. I’d take the lesser of two evils, since there’s no way to even do any work in the first place in America.

(I’ve been depressed for months that the inevitable outcome of all the LLM lawsuits is that book authors will demand licensing fees, which completely kills open source AI models for all but the most funded institutions. I.e. my original work would’ve been completely impossible.)

If it didn't happen, then it's because you are not big enough.

A few cases:

- FB, Instagram, Reddit, Twitter, Youtube are all blocked: user generated content and without outrageous censorship effort (Douyin has 100k employees dedicated to censor and is top cost center, Weibo 50k IIRC) it is very predictable, just assume such things get blocked.

- Google is blocked. They announced in 2010 they didn't want to censor any more.

- A censored version of Bing is not blocked.

- Dropbox is blocked: the same reason.

- Notably, GitHub is NOT blocked. GitHub pages and Gist are blocked. This is because GitHub does censor repos upon government request, plus that when it was once blocked scientist spent effort and managed to reverse the decision.

- WhatsApp and Signal are blocked. SMS from Signal is also blocked for all +86 numbers so people simply can't register.

- iMessage is not blocked. This is, IMO, an outlier. My guess is it's because Apple + no group chat + incompetence.

The problem is rather than, given the economic war between the US and China, the chip ban, the taiwan issues and the GFC, we may not have access to this.

It would send a message of power, and ensure that local tech companies will use a model with their influence rather than western corporate influence.

Not saying they should or would endorse, but it wouldn't be _that_ surprising.

Haha, OK, yeah.

That said, Chinese is an isolating language, so there is little information within a word to hint at the rest of the sentence (no plurality, tense etc.). Which could be better or worse for language models. Or it might not matter in the end given modern tokenization also uses word "fragments" for Western languages.

I don’t think visual information density is much help.

I think it's easier to connect semantic meaning between these tokens/charcaters, because you can relate a character to other characters by their presence in words (most words are 2 characters long (compound words), although there are sometimes words like AB and BC which mean the same thing, but in conversations people will just say A or C and the meaning (AB or BC) is inferred by the context of the conversation, so in a semantic meaning database you would want a connection between A and AB, and to BC & C). You could also have a dimension for each radical that isn't a phonetic component. Radicals do link vaguely related words together, and I think this is a powerful language feature missing in English that could be exploited in the NLP field.

I'm not a fluent or proficient speaker, so maybe I'm not the best person to explain things, but before OpenAI's Whisper, speech-to-text in WeChat in Mandarin was way better than any English speech-to-text I've ever used. The structure of the Chinese language and its large userbase would make me bet long on Chinese NLP progress over the west.

https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat

Look up Byte-Pair Encoding (BPE) for a fascinating and simple algorithm that explains the process of choosing the tokens. It shows why it doesn't matter how token dense the source language is.

It'll be interesting to see how LLMs do when trained on concepts (ie the Chinese alphabet), rather than sounds, though.

From my understanding (which only comes from reading HN), unique token count isn't an issue that LLMs run into. If it is, then that would be a bummer for the possibilities of exploiting all the features of the Chinese language.

> and if you use, say, pinyin

Well yeah pinyin would be inefficient because you're stripping away all the "built in" semantics of a Chinese character to only focus on the pronounciation.

Granted, that doesn't apply to Chinese, which encoders concepts, so that's interesting to see in LLMs.

I've learned languages with alphabets and glyph scripts like Chinese, and *what I don't like about alphabet languages like English is that the letters themselves provide little context to their meaning, although you could learn the latin root words and guess from there. Of course you know how to say it, but you don't know what it means. With Chinese, you might guess what it means, but you don't know how to say it.* The characters have more meaning in them, although this isn't the case for every word, which is something non-speakers assume.

In short, from my experience learning Chinese was way easier than learning Vietnamese, which have historical ties together since Vietnamese used a fork of Chinese script until the early 1900s when they transitioned to latin script in order to improve literacy rates. Sure, it improved literacy rates because to read you only need to know how to pronounce, but it doesn't mean glyph languages are harder to learn the meaning/semantics.

Also, in case of english language, it's one token per one word (more or less), so it's the same as in Chinese - assuming both LLM tokenisers were geared towards their native language.

The only issue is when you have an tokeniser geared towards western languages, and you try to to use the same tokeniser on a different group of languages - then a single word in a language foreign to the tokeniser would have multiple tokens.

But that has nothing to do with the underlying structure of the language.

In other words - you wouldn't really see a difference between an input in chinese compared to english after the text gets tokenised. It's rougly the same amount of tokens, and the underlying parameter count would be also similar.

- if by efficiency you mean the speed of operation, then it's the network size that determines it

- the input token / vector size is not dependent on the language

My mental model of LLMs being next-word predictors with a long context window suggests they don't. Are there any papers on this?

On the other hand, no matter how large the model they struggle to generalize between sentences of the form "George Washington was the first US president" and "The first US president was George Washington".

Clearly generalization behavior is unintuitive, you can come up with a post hoc explanation (weights are independent between early and late layers) but I doubt anyone would have thought ahead of time that models would have an easier time generalizing between languages than between slightly different ways of saying the same thing in English.

[1] https://arxiv.org/pdf/2308.03296.pdf

According to that paper, people made decisions more emotionally in their native languages compared to their second language - where they tend to be more logical.

In case of LLMs there were some nice cases where GPT gave different replies depending on the language of the query. Factual information was roughly the same, but in some cases the model gave totally different replies depending on the language of the query.

As a bilingual speaker - my experience is that the replies are very similar regardless of whether I speak in Polish or English, or mix them both within the same sentence.

Yes, LLMs really encode knowledge. This is not up for debate, and it's obvious to anyone working in AI. It outputs words and input words, yes, but the magic is what is in between. The words are broken down immediately at the first layer. Next it involves 100s of billions of parameters doing god knows what. But it's safe to assume they encode the base reality as best it can. It is what i call 'language-induced reality model'.

The task of next word predictor in a humongous dataset turns out, is best solved by creating an inner representation of the real world. It's still not a perfect model since it's just induced by language though, so obviously it has limitations. But it's really modeling it, and with higher fidelity than people imagined you can learn the world through words alone.

https://gwern.net/scaling-hypothesis#why-does-pretraining-wo...

America is throwing out their chances of making open source LLMs. Copyright holders are demanding license fees for training. That’s analogous to someone demanding licensing fees before you make a YouTube video. No one would be able to do it for free. Whereas it was completely possible to train a high grade LLM for free (clusters are surprisingly accessible for researchers) and it wasn’t till recently that you had to worry about being sued for it.

Net result: open source LLMs die, except for companies that can open source their smaller (lamer) models as an upsell for the real ones that anybody cares about. That’s not a world where open source makes a big impact. That’s a world where (metaphorically) GPL software is subservient to business interests for the rest of eternity. Say what you will about whether that’s true, but no business has influence over Emacs, and it’s fantastic, powerful software. No one will be able to make the equivalent open source fantastic LLM in America at this rate.

I fine tuned GPT 1.5B on chess games. AI Dungeon fine tuned on fantasy novels. All of this type of work will become impossible with the specter of lawsuits hovering over their heads.

EDIT: also most impactful older models were by one person (e.g. Yolo). What I like about ML is that lone wolves can have a big impact.

Take TRC for example. They give people access to TPUs in exchange for being cited. But if they were cited as facilitating large scale piracy, they probably wouldn’t be happy. It could even lead to a lawsuit on the grounds of facilitating copyright infringement, which will likely be the charge against me if someone gets mad enough to sue me directly. (I never distributed anything, but that doesn’t matter if they can prove facilitation.) And TRC is an even juicier target for lawsuits since Google is a giant loot box of money for them.

Piracy generally means taking a full fledged product of someone elses and using it. Seemingly in your description, you're taking the data illegally and then doing all the compute on it. The amount of compute needed in this case is staggering, so you are back to coordinating with other people. Any one of those individuals turning against the group would likely doom the group, hence it's a high risk operation.

They would be more than happy if everyone on earth had every Chinese work in existence.

Even that is essentialist. In some areas, sharing IP is beneficial to the originators and incentives are aligned. In other areas, not so much.

Duxiu makes money selling access to their collection of scanned books. Their incentives are not aligned with having others use those scans for free. Currently, such cases seem to be mostly handled under the most general provisions of the Anti-Unfair Competition Law (反不正当竞争法), but new amendments are likely to make it more explicitly illegal https://www.whitecase.com/insight-alert/china-releases-draft... ("Improperly obtaining or using another business operator's commercial data")

Such a dream is completely impossible when rightsholders demand everyone pay for training data, rather than sharing in the profits of the result. It’s backwards. The actual market profit is what matters. But authors are frothing at the mouth, upset that their work is being used at all without their explicit consent. And some of those authors, like Sarah Silverman and more recently former governor Mike Huckabee, are going after LLM companies and demanding injunctions preventing them from using books at all, under any circumstance, unless they go through authors. Good luck going through 100,000 different authors.

The net result is that we’re headed for a world where the YouTube of LLMs will dominate the space, and no one else will be able to run their own platforms. I’ve done everything humanly possible to try to steer us away from that outcome — you like using stable diffusion yourself? Imagine if no one released stable diffusion in the first place. But things are not looking good.

Lawsuit against Eleuther for books3, courtesy of Huckabee: https://news.ycombinator.com/item?id=37962244

Example of authors being furious, though thankfully with an exception for academic curiosity: https://news.ycombinator.com/item?id=37949012

Twitter is completely merciless. Digging up some examples to illustrate the point…

Here’s one: look through the replies of someone who released a 3D dataset. https://x.com/mattdeitke/status/1678855859089326080?s=61&t=j... "Eat shit Matt" (5O likes) "Hope you enjoy the storm" "you make me sick" and on and on. I can tell you firsthand that this isn’t unusual; it’s the default reaction now.

I like the way you think, can I also get free books at the store if I'm just reading them for learning and will only make money with the knowledge later, or if I promise that I won't put it in practice?

I did a similar deal in university but had to return books to the library every week so I like your plan better.

Even worse, for e-books, libraries are often limited to how often they can lend the book out, and this was done since physical books "degrade" over time, and so have a limited lend-life, the idea is that the same should hold true for digital books as well…

I get the point behind your sarcasm of the first sentence, since that is the essence of the problem.

If you really need to take the books back with you, that's what the library copy machine is for.

It's attitudes like these from the AI sycophants that makes people despise the technology and anyone involved in any of it. I don't give a shit how difficult not having free unfettered access to everyone else's work makes it for you. I don't care that we're supposedly holding the world back (from infinite machine-generated spam made for the sole purpose of making everyone else's lives maximally miserable).

Then there is the harm argument. What is the harm to authors of letting AI models do this? Models aren’t replacing authors any time soon. And even if they were, you won’t be able to stop it; precisely zero times in history have people stood against a wave of new technology. Technology wins 10 out of 10 times in the long run.

I am skeptical that authors deserve anything for merely having a transformative use of their work. But I’m also sympathetic. The key is for authors to get a slice of the actual market value. Right now authors are shutting everyone down merely for using their work at all, with no consideration of whether anyone is even profiting.

Yet even when authors deserve a cent, how do you decide which authors get how many cents? Do all profits go to copyright holders, as with YouTube? Then there’s no room to run a service. Is half fair? Before or after company expenses? And whatever is allocated, is it distributed evenly among all 100,000 authors, whether they’re Stephen King or just an enthusiastic contributor to AO3? $1M in profits distributed to 100,000 authors is a whopping $10 per author. That’ll buy them a Happy Meal and fries. It’s far more lucrative to make up imaginary damages and sue them in court.

It’s immensely complicated, and it’s not at all clear that it’s evil or even exploitative to train AI models on other people’s work.

Counterpoint: Someone who teaches himself how to do something entirely via freely borrowed books from the library, and then makes a living out of those learned skills, is normally not considered stealing.

I'll tell you the simple answer. They don't want to pay for the books because they can get it for free. Simple as that.

You get paid in exchange - you give them your text, they give you their AI. Have you tried Mistral-7B, it can do amazing feats on a 5 year old GPU. Didn't imagine my old GPUs were going to get so smart someday.

LLMs by their nature give every user what they need, adapting and customizing the repository of human knowledge to a particular situation. That's how they pay back, they adapt to serve our needs.

Now the authors can use AI to help themselves - brainstorm ideas, critique, first drafts, etc. But in order to benefit they got to use it first.

For example, did you know that there is a trend for physics textbooks to be dedicated to Xi Jinping? If you didn't know this, because who has time to sift through millions of books to find undesirable biases, isn't it clear that your LLM will develop subtle but favourable biases towards the CCP?

But no, I don’t think governments are accomplices. The smartest move would be for China to wait and see how this plays out. It could significantly shift the epicenter of AI work to China. The single biggest limiting factor for open source LLMs is data, and no one in America or Europe wants to be sued.

High-speed access available for anyone who can do at-scale text extraction, or who can supply us with new collections.

Please focus on your opsec. The more visible you become, the progressively angrier people will get. Don’t do anything silly like edit your Wikipedia page from your house.

With that out of the way, someone I know happens to have the original books3 epub files. I think they can be convinced to send them to you. It’s only 200,000 books, but that could theoretically grow your collection by 10% or so. I don’t know whether that would be helpful to you (you’ve far surpassed books3 at this point), but if so, let me know.

Given the legal risks, the best course of action for AI companies is probably to ignore English and European books entirely. There is plenty of Chinese data, and the models would learn all the same concepts without exposing anyone to lawsuits.

Basically, you download a client, say "allocate 2 TB of my disks to whatever archive.org/donate/disk.rss" says, and the server/client combination ensures you download and seed the rarest 2TB of the collection.

This design is also open, in the sense that the server can share the database of torrents it contains, and anyone can use it to fetch any of the files in the dataset from the swarm.

Would something like this be at all useful? I've emailed a few archivists, but I got no response, and the one person I've managed to talk to about this said there have been a few attempts on this, but they always fail for one reason or another.

The hard part is that those who donate their space have authority over that space. It’s the Byzantine fault tolerance problem: imagine if 4chan donated their space, then started serving CSAM instead of the expected data. You can use hashes to verify integrity, but then the question becomes who gets to decide which hashes are ok. And hashing makes it impossible to edit large files, which is a frequent occurrence in LLM work. You’re constantly tweaking your datasets and spitting out new blobs.

Direct answer: yes, you’re doing good work, and you should keep doing it. I would personally use this for storing books3 transformations.

The other hard part is that you’ll want at least some redundancy — see 6.824 distributed systems, or the GFS paper. It’s why I’ve been implementing Raft and toying with some kind of distributed consensus without a blockchain. (Such consensus is still possible if the researchers were granted authority over what can be stored — which is the whole reason people are donating their disk space in the first place.)

Another issue is sudden bandwidth loss. Data storage is one part of it. The other half is rapid transfer. By replicating the data, you can pull it from multiple replicas at once (I.e. there are more seeders). This also protects against someone suddenly getting throttled, or just having a power outage. The protocol should prioritize donors with high bandwidth over vast storage space.

Feel free to DM me on Twitter if you’d like to toss around some design ideas more seriously, and thank you for trying to build this.

If you're doing this in bittorrent then you might want a client that's configured to optimize for a different goal than most torrent clients.

Potential goals, somewhat conflicting:

A) keep data with a low mirroring degree available. either this needs to be centrally coordinated or some sort of randomized algorithm where clients pick underseeded torrents but not everyone picks the same

B) bandwidth matching. to not consume more resources than are provided a client maybe should only download 1 piece of data for every N times it uploaded any piece. This is much less greedy than what you'd have in a normal torrent client but ensures that caches themselves don't take up much bandwidth compared to users who actually want to download something. Otherwise a misconfigured cache (e.g. behind NAT) could accidentally always download data without ever giving much back.

Edit: Looks like Twitter wants me to pay to DM, I'll email you instead.

I responded with some telegram info if that helps.

Short summary of The Right to Read, and evidently the world you want.