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I agree that lawmaking has never been a process under any kind of statistical control, and so its hard to say what any change to the system is really doing.

That said, I can only really agree that it might be a net good IF the weights (and all other data necessary to reproduce the summaries) were open knowledge. Without that, you are just shipping a bill off to a black box and trusting that the gremlins in the box are acting in good faith. If every legislative office is doing this, then those gremlins will become the new focal point of a basically-invisible form of lobbying. This is good for OpenAI (and whoever controls it) but probably bad for everyone else.

> ChatGPT can read a 4,000 page bill in 3 days

So can the legislative staff of the committee of responsibility.

> And it'll be a nice neutral way of arguing about what is in a bill - people might be able to agree on which AI is reasonable

Ha ha ha ha. No. Just like they can’t do that with human experts, and for the same reason. Heck, this has already been demonstrated in the case of AI models quite resoundingly.

And it would be a bad thing if they could: designating a single AI vendor as the sole arbiter of truth in the lawmaking process would be much worse than the status quo.

> ChatGPT can read a 4,000 page bill in 3 days.

Can it, though? The context window is nowhere near 4,000 pages. Getting ChatGPT to understand and summarize such a long document seems nontrivial.

And with most bills I've read, the devil is 100% in the details. Of those 4000 pages, it might just be one sentence that has far reaching ramifications. Alternatively, folks will start maliciously crafting bills in a way that the ChatGPT summary misses the key parts.

That's less of a GPT problem and more of a human problem: bills/contracts/T&Cs/ToS have all been massively blown out because: * Companies need to cover every minute detail/situation to prevent getting boned, especially in America where your citizens will sue at the drop of a hat * Companies want to purposefully bulk out the contract so that most people don't bother reading it

The new SEO will be LEO :)

(AIO sounds less catchy)

Maybe LLEO or LLMEO as LEO is already semi-widely known as Law Enforcement Officer.

It probably can, assuming that a bill can be separated into smaller parts, more or less independent of each other. Trusting the summary is another story.

But expecting people to read 4000 pages in 3 days and make informed decisions after that is unrealistic, regardless of whether they are using lossy untrusted compression tools like ChatGPT.

"Trusting the summary is another story." What story could we possibly be interested in that involves having chat GPT read a bill but we don't care about a summary of it being accurate?

Add an intern in the loop that feeds every chapter separately and then compiles per-chapter summary that can then be summarized again if it is too long or has too many interdependencies.

super trivial actually. Just tokenize the document using Llama index and pass the vectorization instead.

I don't know about GPT 4, but I did not find GPT 3.5 great for summarizing long documents. I tried it on two long that I knew the content on, and it did not pick up the main themes well. Not even well enough that it would be believable at a glimpse, but I would be more concerned if it was believable and used as the basis of a debate. GPT 4 should do a little bit better in that regard, since it allows for longer input strings, but still not long enough to properly process 4,000 pages.

> It'll probably be a net win once the dust settles if it makes it harder to ram stuff through without debate

Or make it worse, because it completely normalises the practice of foisting long revisions on people at the last minute "you had plenty of time to understand it; my computer read it in seconds!" and encourages people rely more on tooling than dividing reading text between themselves and people they trust who (unlike GPT) have a shared interest in achieving particular goals and protecting particular parties.

From what we've seen current gen summarising tools aren't particularly accurate at summarising legal documents, never mind highlighting stuff which is politically sensitive, and that's before we get into clauses inserted adversarially by entities who have the same tools to test whether GPT summaries pick them up

Your comment reminds me of how HFT is now in a similar regime -- speed is everything, and whoever can trade with the lowest latency, wins.

ChatGPT _can't_ read a 4000 page bill in 3 days. It can't even read a 10 page not-very dense technical document without hallucinating wildly in its summary in my experience with ChatGPT4.

> I believe that for some of the largest expenditures of my lifetime the US Congress voted literally without people having read the final text bills they were voting on.

Congress is similar to most other large organizations, such as large software companies. In a large company software company the upper level managers don't actually read all the code. They make their decisions based what managers below them report, and so on down the management chain.

Representatives and Senators are Congress' equivalent to those upper level managers.

The House has over 6000 staff members, ranging from staff working for individual representatives to staff employed by committees. The Senate has over 4000 staff members. Those are the people that deal with nitty gritty details of the bills.

> ... people might be able to agree on which AI is reasonable and what it thinks the summary should be.

We're going to wind up with ChatDNC and ChatRNC. Nobody in office is going to agree on which one is reasonable, for the same reason that they refuse to agree on anything today.

I agree with the first part of your comment. I'm not sure I can follow you where you take it though.

> ChatGPT has a nice clear style and will probably help make these laws more accessible.

If the laws are not meant to be read in the first place (which I agree with as a likely pattern), how is this motivating? I'm skeptical that they're not read because of their density. I'd flip the causality; they're dense because they're not to be read.

> nice neutral way of arguing about what is in a bill

Elon Musk is already talking about "TruthGPT", an AI without "liberal bias". I don't think you will find anything resembling neutrality in this conversation.

To put all political opinions aside, I was also about to add that it is fundamentally wrong that representatives in a representative democracy are outsourcing lawmaking to a private company whose training data and associated reinforcement learning may contain all sorts of unknown biases. Even if all biases would be absent, I would argue that elected representatives are neither doing their jobs nor serving their constituencies if they outsource policy deliberation and lawmaking to a machine owned by a private company.

> Elon Musk is already talking about "TruthGPT", an AI without "liberal bias". I don't think you will find anything resembling neutrality in this conversation.

Between TruthGPT, Chuck Schumer & Mitch McConnell I would expect the most honest summary to be TruthGPT. I doubt it'd be close. Even with a very healthy dose of cynicism towards all billionaire projects and scepticism of the abilities of AI. If nothing else the AI has a big advantage because it would have read and possibly remembered the text of the bill and could answer specific questions which already gives it a massive edge over its geriatric competition.

The politicians lie proactively with plausible deniability and/or people wilfully ignoring it because of team dynamics. Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

> Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

See, I consider the functional definition of what current generation LLMs do to be "lying". Not out of malice or moral failing (of the model or the model's creators), but simply out of lack of control/alignment/modulation systems that people typically associate with honesty, integrity, and intention.

The problem-model behind these systems is "predict what comes next". The objective in training is not accuracy of content (by whatever definition...), but verisimilitude of output. An LLM happily improvises on whatever seed you give it. Sometimes this leads to output that aligns with someone's definition of reality. Sometimes it's hallucination or bullshit or garbage or whatever you term you want to use for it. You can modulate this with better prompt engineering, but only by degrees.

I have no doubt future generations of these systems will start tackling this, but the systems as they stand now are by their very nature, liars.

edit: And additionally, while I can't necessarily argue against your trust in these systems (especially given the choice you presented.. I'd probably choose XGPT too), "neutrality" is defined by a shared trust that I don't see a route to in the current climate.

> Between TruthGPT, Chuck Schumer & Mitch McConnell I would expect the most honest summary to be TruthGPT

Since “TruthGPT” has an overt mission (“anti-woke”) to lean even harder into biases existing LLMs have demonstrated, I... would expect the opposite.

> Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

ChatGPT lies all the time, and has no innate access to experimental evidence.

> Since “TruthGPT” has an overt mission (“anti-woke”) to lean even harder into biases existing LLMs have demonstrated, I... would expect the opposite.

I'd find it interesting to chat to an LLM which had pre-training and fine-tuning for instruction-following, but no attempt to train it to be "safe" or "unbiased" or "truthful". Of course, it is going to repeat whatever biases exist in its pre-training data – but it would be an interesting way of exploring what those biases actually are.

I wonder if Musk has thought of using the algorithm of Twitter Community Notes (formerly Birdwatch). Essentially, its algorithm treats a claim as true when people who normally disagree on what is true agree that it is true. If people at opposite ends of the political spectrum agree X is true, it probably is true, and its truth is unlikely to be contested or controversial. So you could use human reviewers to build such a dataset of non-controversial truths, and then fine-tune an LLM to endorse them. When it comes to the opposite kind of statement, the kind of highly controversial claim on which there is widespread disagreement, there are two possible strategies: (1) explicitly train it to avoid taking sides, (2) don't do anything and just let it answer according to the biases of its pre-training data, whatever that may be.

That way you could train an LLM to not be "woke" (or at least no more "woke" than its pre-training dataset is), without training it to be "anti-woke". It would be interesting to talk to such an LLM and see what kind of opinions it may endorse.

MAGA and Bernie fans both agree that Biden is the devil.

ChatGPT CAN'T NOT lie even if it wanted to, because it has no concept of truth or false, it's just generating sentences. A lie is just as valid a sentence to construct from a prompt as an accurate statement.

A calculator also has no concept of true or false.

> Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

ChatGPT "lies" even when you don't want it to. It's true that it's not a strategic liar like a regular politician, and liable to give the game away by accidentally telling the truth, but it'll happily refine your lies or make up coherent nonsense pretty much anywhere it hasn't been trained specifically not to

"ChatGPT can read a 4,000 page bill in 3 days."? in 3 minutes?

Of course, but the statement was likely a jab at an organic congressperson's inability to read a bill in even three days, before going ahead and signing it anyway.

Actually it takes about 10-30 seconds (one request) for GPT4 to summarize 2000 words (a page or two). And there is a limit of 25 requests every 3 hours for GPT4, and no API access. So this might even take significantly longer than 3 days.

GPT4 does have an API with a 32k token token window. That's 25,000 words.

I don't have access to it, and I'm guessing neither do the 40 ChatGPT Plus licenses that Congress got.

You know most of the cells in your body have no idea what your brain is thinking.

There's no world in which you'll ever get an organism to know everything top down.

So I have to assume the remaining conclusion you have is a naive understanding of organizational structure and super organisms.

The problem with human 'super organism' is that nobody really wants to be the skin cells under the foot to be stepped on and sacrificed.

> And it'll be a nice neutral way of arguing about what is in a bill - people might be able to agree on which AI is reasonable and what it thinks the summary should be.

It's more likely that they'll further politicize these models.