Idea for LLM support for writing LessWrong posts: virtual comments.

Back in August I discussed with Rafe & Oliver a bit about how to integrate LLMs into LW2 in ways which aren't awful and which encourage improvement---particularly using the new 'prompt caching' feature. To summarize one idea: we can use long-context LLMs with prompt caching to try to simulate various LW users of diverse perspectives to write useful feedback on drafts for authors.

(Prompt caching (eg) is the Transformer version of the old RNN hidden-state caching trick, where you run an input through the (deterministic) NN, and then save the intermediate version, and apply that to arbitrarily many future inputs, to avoid recomputing the first input each time, which is the naive way to do it. You can think of it as a lightweight finetuning. This is particularly useful if you are thinking about having large generic prompts---such as an entire corpus. A context window of millions of tokens might take up to a minute & $1 to compute currently, so you definitely need to be careful and don't want to compute more than once.)

One idea would be to try to use LLMs to offer feedback on drafts or articles. Given that tuned LLM feedback from Claude or ChatGPT is still not that great, tending towards sycophancy or obviousness or ChatGPTese, it is hardly worthwhile running a post through a generic "criticize this essay" prompt. (If anyone on LW2 wanted to do such a thing, they are surely capable of doing it themselves, and integrating it into LW2 isn't that useful. Removing the friction might be helpful, but it doesn't seem like it would move any needles.)

So, one way to force out more interesting feedback would be to try to force LLMs out of the chatbot assistant mode-collapse, and into more interesting simulations for feedback. There has been some success with just suggestively-named personas or characters in dialogues (you could imagine here we'd have "Skeptic" or "Optimist" characters), but we can do better. Since this is for LW2, we have an obvious solution: simulate LW users! We know that LW is in the training corpus of almost all LLMs and that writers on it (like myself) are well-known to LLMs (eg. truesight). So we can ask for feedback from simulated LWers: eg. Eliezer Yudkowsky or myself or Paul Christiano or the author or...

This could be done nicely by finetuning a "LW LLM" on all the articles & comments, with associated metadata like karma, and then feeding in any new draft or article into it, and sampling a comment from each persona. (This helps instill a lot of useful domain knowledge, but also, perhaps more importantly, helps override the mode-collapse and non-judgmentalness of assistant LLMs. Perhaps the virtual-gwern will not be as acerbic or disagreeable as the original, but we'll take what we can get at this point...) If there is some obvious criticism or comment Eliezer Yudkowsky would make on a post, which even a LLM can predict, why not deal with it upfront instead of waiting for the real Eliezer to comment (which is also unlikely to ever happen these days)? And one can of course sample an entire comment tree of responses to a 'virtual comment', with the LLM predicting the logical respondents.

This can further incorporate the draft's author's full history, which will usually fit into a multi-million token context window. So their previous comments and discussions, full of relevant material, will get included. This prompt can be cached, and used to sample a bunch of comment-trees. (And if finetuning is infeasible, one can try instead to put the LW corpus into the context and prompt-cache that before adding in the author's corpus.)

The default prompt would be to prompt for high-karma responses. This might not work, because it might be too hard to generate good high-quality responses blindly in a feedforward fashion, without any kind of search or filtering. So the formatting of the data might be to put the metadata after a comment, for ranking purposes: so the LLM generates a response and only then a karma score, and then when we sample, we simply throw out predicted-low-score comments rather than waste the author's time looking at them. (When it comes to these sorts of assistants, I strongly believe 'quality > quantity', and 'silence is golden'. Better to waste some API bills than author time.)

One can also target comments to specific kinds of feedback, to structure it better than a grab-bag of whatever the LLM happens to sample. It would be good to have (in descending order of how likely to be useful to the author) a 'typo' tree, a 'copyediting'/'style'/'tone' tree, 'confusing part', 'terminology', 'related work', 'criticism', 'implications and extrapolations', 'abstract/summary' (I know people hate writing those)... What else? (These are not natural LW comments, but you can easily see how to prompt for them with prompts like "$USER $KARMA $DATE | Typo: ", etc.)

As they are just standard LW comments, they can be attached to the post or draft like regular comments (is this possible? I'd think so, just transclude the comment-tree into the corresponding draft page) and responded to or voted on etc. (Downvoted comments can be fed back into the finetuning with low karma to discourage feedback like that.) Presumably at this point, it would not be hard to make it interactive, and allow the author to respond & argue with feedback. I don't know how worthwhile this would be, and the more interaction there is, the harder it would be to hide the virtual comments after completion.

And when the author finishes writing & posts a draft, the virtual comments disappear (possibly entirely unread), having served their purpose as scaffolding to help improve the draft. (If the author really likes one, they can just copy it in or quote it, I'd think, which ensures they know they take full responsibility for it and can't blame the machine for any mistakes or confabulations or opinions. But otherwise, I don't see any real reason to make them visible to readers of the final post. If included at all, they should prominently flagged---maybe the usernames are always prefixed by AI_$USER to ensure no one, including future LLMs, is confused---and definitely always sort to the bottom & be collapsed by default.)

Should you write text online now in places that can be scraped? You are exposing yourself to 'truesight' and also to stylometric deanonymization or other analysis, and you may simply have some sort of moral objection to LLM training on your text.

This seems like a bad move to me on net: you are erasing yourself (facts, values, preferences, goals, identity) from the future, by which I mean, LLMs. Much of the value of writing done recently or now is simply to get stuff into LLMs. I would, in fact, pay money to ensure Gwern.net is in training corpuses, and I upload source code to Github, heavy with documentation, rationale, and examples, in order to make LLMs more customized to my use-cases. For the trifling cost of some writing, all the worlds' LLM providers are competing to make their LLMs ever more like, and useful to, me.

And that's just today! Who knows how important it will be to be represented in the initial seed training datasets...? Especially as they bootstrap with synthetic data & self-generated worlds & AI civilizations, and your text can change the trajectory at the start. When you write online under stable nyms, you may be literally "writing yourself into the future". (For example, apparently, aside from LLMs being able to identify my anonymous comments or imitate my writing style, there is a "Gwern" mentor persona in current LLMs which is often summoned when discussion goes meta or the LLMs become situated as LLMs, which Janus traces to my early GPT-3 writings and sympathetic qualitative descriptions of LLM outputs, where I was one of the only people genuinely asking "what is it like to be a LLM?" and thinking about the consequences of eg. seeing in BPEs. On the flip side, you have Sydney/Roose as an example of what careless writing can do now.) Humans don't seem to be too complex, but you can't squeeze blood from a stone... ("Beta uploading" is such an ugly phrase; I prefer "apotheosis".)

This is one of my beliefs: there has never been a more vital hinge-y time to write, it's just that the threats are upfront and the payoff delayed, and so short-sighted or risk-averse people are increasingly opting-out and going dark.

If you write, you should think about what you are writing, and ask yourself, "is this useful for an LLM to learn?" and "if I knew for sure that a LLM could write or do this thing in 4 years, would I still be doing it now?"

...It would be an exaggeration to say that ours is a hostile relationship; I live, let myself go on living, so that Borges may contrive his literature, and this literature justifies me. It is no effort for me to confess that he has achieved some valid pages, but those pages cannot save me, perhaps because what is good belongs to no one, not even to him, but rather to the language and to tradition. Besides, I am destined to perish, definitively, and only some instant of myself can survive in him. Little by little, I am giving over everything to him, though I am quite aware of his perverse custom of falsifying and magnifying things.

...I shall remain in Borges, not in myself (if it is true that I am someone), but I recognize myself less in his books than in many others or in the laborious strumming of a guitar. Years ago I tried to free myself from him and went from the mythologies of the suburbs to the games with time and infinity, but those games belong to Borges now and I shall have to imagine other things. Thus my life is a flight and I lose everything and everything belongs to oblivion, or to him.

Concrete benchmark proposals for how to detect mode-collapse and AI slop and ChatGPTese, and why I think this might be increasingly important for AI safety, to avoid 'whimper' or 'em hell' kinds of existential risk: https://gwern.net/creative-benchmark EDIT: resubmitted as linkpost.

I have some long comments I can't refind now (weirdly) about the difficulty of investing based on AI beliefs (or forecasting in general): similar to catching falling knives, timing is all-important and yet usually impossible to nail down accurately; specific investments are usually impossible if you aren't literally founding the company, and indexing 'the entire sector' definitely impossible. Even if you had an absurd amount of money, you could try to index and just plain fail - there is no index which covers, say, OpenAI.

Apropos, Matt Levine comments on one attempt to do just that:

Today the Wall Street Journal has a funny and rather cruel story about how SoftBank Group went all-in on artificial intelligence in 2018, invested $140 billion in the theme, and somehow … missed it … entirely?

The AI wave that has jolted up numerous tech stocks has also had little effect on SoftBank’s portfolio of publicly traded tech stocks it backed as startups—36 companies including DoorDash and South Korean e-commerce company Coupang.

This is especially funny because it also illustrates timing problems:

SoftBank missed out on huge gains at AI-focused chip maker Nvidia: The Tokyo-based investor put around $4 billion into the company in 2017, only to sell its shares in 2019. Nvidia stock is up about 10 times since.

Oops. EDIT: this is especially hilarious to read in March 2024, given the gains Nvidia has made since July 2023!

Part of the problem was timing: For most of the six years since Son raised the first $100 billion Vision Fund, pickings were slim for generative AI companies, which tended to be smaller or earlier in development than the type of startup SoftBank typically backs. In early 2022, SoftBank nearly completely halted investing in startups when the tech sector was in the midst of a chill and SoftBank was hit with record losses. It was then that a set of buzzy generative AI companies raised funds and the sector began to gain steam among investors. Later in the year, OpenAI released ChatGPT, causing the simmering interest in the area to boil over. SoftBank’s competitors have spent recent months showering AI startups with funding, leading to a wide surge in valuations to the point where many venture investors warn of a growing bubble for anyone entering the space.

Oops.

Also, people are quick to tell you how it's easy to make money, just follow $PROVERB, after all, markets aren't efficient, amirite? So, in the AI bubble, surely the right thing is to ignore the AI companies who 'have no moat' and focus on the downstream & incumbent users and invest in companies like Nvidia ('sell pickaxes in a gold rush, it's guaranteed!'):

During the years that SoftBank was investing, it generally avoided companies focused specifically on developing AI technology. Instead, it poured money into companies that Son said were leveraging AI and would benefit from its growth. For example, it put billions of dollars into numerous self-driving car tech companies, which tend to use AI to help learn how humans drive and react to objects on the road. Son told investors that AI would power huge expansions at numerous companies where, years later, the benefits are unclear or nonexistent. In 2018, he highlighted AI at real-estate agency Compass, now-bankrupt construction company Katerra, and office-rental company WeWork, which he said would use AI to analyze how people communicate and then sell them products.

Oops.

tldr: Investing is hard; in the future, even more so.