- cross-posted to:
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.ml
Artificial intelligence is worse than humans in every way at summarising documents and might actually create additional work for people, a government trial of the technology has found.
Amazon conducted the test earlier this year for Australia’s corporate regulator the Securities and Investments Commission (ASIC) using submissions made to an inquiry. The outcome of the trial was revealed in an answer to a questions on notice at the Senate select committee on adopting artificial intelligence.
The test involved testing generative AI models before selecting one to ingest five submissions from a parliamentary inquiry into audit and consultancy firms. The most promising model, Meta’s open source model Llama2-70B, was prompted to summarise the submissions with a focus on ASIC mentions, recommendations, references to more regulation, and to include the page references and context.
Ten ASIC staff, of varying levels of seniority, were also given the same task with similar prompts. Then, a group of reviewers blindly assessed the summaries produced by both humans and AI for coherency, length, ASIC references, regulation references and for identifying recommendations. They were unaware that this exercise involved AI at all.
These reviewers overwhelmingly found that the human summaries beat out their AI competitors on every criteria and on every submission, scoring an 81% on an internal rubric compared with the machine’s 47%.
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Well, not every metric. I bet the computers generated them way faster, lol. :P
And for a much much smaller paycheck.
All corporate gives af about.
It might be all I care about. Humans might always be better, but AI only has to be good enough at something to be valuable.
For example, summarizing an article might be incredibly low stakes (I’m feeling a bit curious today), or incredibly high stakes (I’m preparing a legal defense), depending on the context. An AI is sufficient for one use but not the other.
And you can absolutely trust that tons of executives will definitely not understand this distinction and will use AI even in areas where it’s actively harmful.
They’ll use it until it blows up in their faces and then they will all backtrack. Executives are like startled cattle.
Let’s not act like executives are the only morons in this world. Plenty of rank and file are leaning on AI as well.
Sometimes I am preparing a high stakes communication for work and struggling for brevity. I will ask AI for help reducing my word count and I find it is helpful as an impartial editor. I take its 25% reduction, sigh, accept most of what it sacrificed, fix a word or two, and am done. It’s helpful.
I mean, what you’re essentially implying is, what if we could do a lot of things that we do today, but faster and less quality.
Imo we have too much things today and very few are worth their salt, so this is the opposite of the right direction.
That’s not what I’m implying. What I’m saying is that wasting time and effort on quality is pointless when the threshold for success is low.
For example, I could use aerospace quality parts (perfectly machined to micron-level tolerances) to build a toaster. However, while this would not increase the performance meaningfully, the cost would be orders of magnitude greater. Instead I can use shitty off-the-shelf parts because it doesn’t really make a difference.
Maybe in other words, engineering tolerances apply to LLMs too. They’re crude devices, but it’s totally fine if you have a crude problem.
That’s not what I’m implying. What I’m saying is that wasting time and effort on quality is pointless when the threshold for success is low.
Yes and my response to that is for some people maybe, for others they don’t want a low threshold, they want few good articles instead of spam of low quality.
Maybe in other words, engineering tolerances apply to LLMs too. They’re crude devices, but it’s totally fine if you have a crude problem.
Exactly, I’m saying there is no objective crude problem. You might be okay with simple summaries but I want every single piece of information I consume to have a very high bar.
What if you’re reading Lemmy, and you don’t really feel like reading the article. Is the headline likely to tell you all you need to know or is the ai summary likely to find more info and without the clickbait?
Imo it’s on me to either read the article or be okay with not being informed. Don’t get me wrong, a summery is good, but not when it’s not reliable and the article is a click away, some might have a different comfort level.
Sure, go for it. But good luck paying an army of copywriters to summarize every article you read.
No summery is better than a bad summery, it would encourage you to actually read the source.
Part of the time.
This is a really valid point, especially because it’s not only faster but dramatically cheaper.
The thing is, summaries which are pretty terrible might be costly. If decision makers are relying on these summaries and they’re inaccurate, then the consequences might be immeasurable.
Suppose you’re considering 2 cars, one is very cheap but on one random day per month it just won’t start, the other is 5x the price but will work every day. If you really need the car to get to work, then the one that randomly doesn’t start might be worse than no car at all.
Are we sure it’s cheaper though? I mean it legitimatly might not be. I have some friends who work in tech and they use an AI model for, amongst other things, summarizing information on their internal documentation. They’ve told me what their company is paying for the license to use this thing, and it’s eyewatering. also, uhh last time I checked, the company they got that license from does not turn a profit… so it appears to be too cheap at the moment.
It might really be the case that it isn’t cheaper than just paying someone a normal salary to do that work, and it probably isn’t cheaper than just jamming the work being done by the AI now back onto preexisting employees (which is what they did before ~2 years ago anyway).
The other thing that makes me feel this might not be unreasonable is that everyone on the team likes the tool, except their manager, who has thrown out the idea to cut it twice now (that I know of).
I’ve been curious about this too, but haven’t been able to find anything that puts a real price (including future profit margin) on GenAI. For example, having a chat conversation with a customer service agent in India might cost about $2-3. Is a GenAI bot truly cheaper than that once you factor in the energy & water costs, hardware, training, profits, etc.? It might be, but I’m skeptical.
Right? That’s the entire point.
LLMs == AGI was and continues to be a massive lie perpetuated by tech companies and investors that people still have not woken up to.
Who is claiming that LLMs are generally intelligent? Is it just “they” or can you actually name a company?
I think the idea is that every company is dumping money into LLMs and no other form of alternative AI development to the point that all AI research is LLM based and therefore to investors and those involved, it’s effectively the only only avenue to AGI, though that’s likely not true
You mean the stuff currently peddled everywhere as “Artificial intelligence”?
Yeah, nobody is saying they are intelligent
AI and AGI are not the same thing.
A chess playing robot is intelligent but it’s so called “narrow intelligence” because it’s really good at one thing but that doesn’t translate to other things. Human are generally intelligent because we can perform a wide range of cognitive tasks. There’s nothing wrong at calling LLM an AI because that’s what it is. I’m not aware of a single AI company claiming to posses an AGI system.
Yes, I missed the implied meaning when you said “generally intelligent”
In game NPC actions have been called “AI” for decades. Computers playing chess has been called AI for decades. Lots of stuff has been.
Nobody thought they were genuinely sentient or sapient.
The fact that people lumped LLMs, text-to-image generators, machine learning algorithms, image recognition algorithms, etc into a category and called it “AI” doesn’t mean they think it is self aware or intelligent in the way a human would be.
The person I replied to said nobody was claiming LLMs were intelligent. I just posted that the people behind the push for this overhyped bubble are indeed making that claim
Whether people believe it is something else. But also, many people do believe it
He said generally intelligent, In the context of the first reply using the term AGI. There is a difference between artificial intelligence and artificial general intelligence.
I see… At first read I thought the generally was implying somewhat. I missed the meaning in aGi
The fact that we even had to start using the term AGI when in common parlance AI always meant the same up until recently, shows how goal posts are being moved.
What people mean by AI has been changing for as long as the term has been used. When I was studying CS in the 80s, people said the holy grail was giving a computer printed English text and having it read it aloud. It wasn’t much later that OCR and text to speech software was commonplace.
Generally, when people say AI, they mean a computer doing something that normally takes a human, and that bar goes up all the time.
It might also be a question of how we define “intelligence”. We really don’t have a clear definition and it’s a moving target as we find out more
- “reading aloud is something only a person can do. It requires intelligence”. Here’s a computer doing it. “Oh, that’s not really intelligence, is it”
The thing with ‘common parlance’ is that it’s used by people without a deep understanding of the subject. Among AI researchers, there’s never been confusion about this. We have different terms for different things for a reason. The term AGI has been around since the early 2000s.
It’s like complaining about the terms jig, spoon, spinner, and fly, and saying that back in the day, we just called them fishing lures. They are fishing lures, but these terms describe different types. Similarly, AGI is a form of AI, but it refers to a specific kind.
To a degree, but, like, video game ai has been called that for decades, I don’t think anyone ever thought it was agi. It’s a more specific term, and it saw use before the big LLM craze started
“Just one more training on a social network”
Can’t wait for the bouble to burst.
We shouldn’t wait, it is already basically illegal to sample the works of others so we should just pull the plug now.
The issue with legally pulling the plug is that it won’t stop AI baddies, only good AI companies who respect the law.
The knowledge and tools are still out there.
But when the bouble bursts it will tank AI globally.
good AI companies who respect the law
When those come around maybe we can rethink our stance, but for now we should stop the AI baddies.
Which will only be possible with good old fashioned bouble bursting as I said.
Nah we can start enforcing the laws as they exist. OpenAI is using works of others commercially without permission.
We don’t have to wait.
As I noted, that only works with a limited set of AI companies.
They need to be in the juristiction of whatever government that decide to enforce the laws, if not, there is very little that can be done.
Then, besides needing to be in the right juristiction, the punnishment needs to be large enough that you can’t just budget it away.
Then any country doing this will know that they are deliberately getting rid of an important sector, while other countries will continue running their sectors.
Important? Unlikely.
Ten ASIC staff, of varying levels of seniority, were also given the same task with similar prompts.
This is the key line here. These are likely university educated staff with significant experience in writing and summarising information and they were specifically tasked with this. However, within the social media landscape (Lemmy, reddit, etc) AI is already better at summarising information than humans because most human social media users are fucking retarded and spend their time either a) not reading properly/at all or b) cherrypicking information to fit whatever flavour of impassioned narrative they are trying to sell to everyone else.
Just some very recent examples I’ve seen of Lemmy users proving they are completely incapable of parsing relevant information are that article about an alternative, universal and non-proprietary database called GetGee which everyone seemed to think was an article about whether TikTok should be banned (because the word TikTok was in the title and that tricked their monkey brains) or the update to the 404 Media story on “active listening” in which people responded as if this technology exists and is in use when 404 Media still haven’t been able to confirm either of these things. The second one was particularly egregious because it got picked up by all kinds of tech-related YouTube channels and news sites and regurgitated by their viewers and readers without a single one of these people ever bothering to read the source material properly.
Lemmy users proving they are completely incapable of parsing relevant information
To be fair, you need to actually read the article to be able to summarize it.
I had the same thought. Most people I encounter online and in person are not great at summarizing information regardless of the context.
For example: those who don’t summarize the content of a conversation and instead poorly and inaccurately act out the entire encounter, "word by word ". Ughhhhh.
human social media users are fucking retarded
I feel attacked
The most promising model, Meta’s open source model Llama2-70B, was prompted to summarise the submissions
Llama 2 is insanely outdated and significantly worse than Llama3.1, so this article doesn’t mean much.
On July 18, 2023, in partnership with Microsoft, Meta announced Llama 2 On April 18, 2024, Meta released Llama-3
L2 it’s one year old. A study like that takes time. What is your point? I bet if they would do it with L3 and the result came back similar, you would say L3 is „insanely outdaded“ as well?
Can you confirm that you think with L3, the result would look completely opposite and the summaries of the AI would always beat the human summaries? Because it sounds like you are implying that.
We know the performance of L2-70b to be on par with L3-8b, just to put the difference in perspective. Surely they models continue to improve and we can only hope the same improvements will be found in L4, but I think the point is that models have improved dramatically since this study was run and they have put in way more attention in the fine-tuning and alignment phase of training, specifically for these kinds of tasks. Not saying this means the models would beat the human summaries everytime (very likely not), but at the very least the disparity between them wouldn’t be nearly as large. Ultimately, human summaries will always be “ground truth”, so it’s hard to see how models will beat humans, but they can get close.
Can you confirm that you think with L3, the result would look completely opposite and the summaries of the AI would always beat the human summaries? Because it sounds like you are implying that.
Lemmy users try not to make a strawman argument (impossible challenge)
No, that’s not what I said, and not even close to what I was implying. If Llama 2 scored a 47% then 3.1 would score significantly better, easily over 60% at least. No doubt humans can be better at summarizing but A) It needs someone that’s very familiar with the work and has great English skills and B) It needs a lot of time and effort.
The claim was never that AI can summarize better than people, it was that it can do it in 10 seconds and the result would be “good enough”. People are already doing AI summaries of longer articles without much complaints.
Lemmy users try not to make a strawman argument (impossible challenge)
This was not a strawman. Please don’t assume lemmy users make logical fallacies when it’s only you who thinks that.
I guess you missed the part where he said “Oh you said X but you’re actually implying Y? Did you mean Y? Please confirm you actually meant Y.”
That’s my point, from my perspective, there was no switch. Using a one year old model is fine.
My comment was about how people looking at the same thing, one might think it’s a bait and switch while the other one always knew the second item was being implied.
The headline never said all AI or latest AI.
This is pretty much every study right now as things accelerate. Even just six months can be a dramatic difference in capabilities.
For example, Meta’s 3-405B has one of the leading situational awarenesses of current models, but isn’t present at all to the same degree in 2-70B or even 3-70B.
Just a few more tens of millions of dollars, and it’ll be vastly improved to “pathetic” and “insipid”.
You didn’t bother to Read the article. Read the article. Study was conducted last year
I read the article. I’m aware it’s an older study. Point still stands.
And yet your claim is still pointless unlike this study
Did LLama3.1 solve the hallucination problem?
I bet we would have heard if it had, since It’s the albatross hanging on the neck of this entire technology.
Not a stock market person or anything at all … but NVIDIA’s stock has been oscillating since July and has been falling for about a 2 weeks (see Yahoo finance).
What are the chances that this is the investors getting cold feet about the AI hype? There were open reports from some major banks/investors about a month or so ago raising questions about the business models (right?). I’ve seen a business/analysis report on AI, despite trying to trumpet it, actually contain data on growing uncertainties about its capability from those actually trying to implement, deploy and us it.
I’d wager that the situation right now is full a lot of tension with plenty of conflicting opinions from different groups of people, almost none of which actually knowing much about generative-AI/LLMs and all having different and competing stakes and interests.
“What are the chances…”
Approximately 100%.
That doesn’t mean that the slide will absolutely continue. There may be some fresh injection of hype that will push investor confidence back up, but right now the wind is definitely going out of the sails.
The core issue, as the Goldman - Sachs report notes, is that AI is currently being valued as a trillion dollar industry, but it has not remotely demonstrated the ability to solve a trillion dollar problem.
No one selling AI tools is able to demonstrate with confidence that they can be made reliable enough, or cheap enough, to truly replace the human element, and without that they will only ever be fun curiosities.
And that “cheap enough” part is critical. It is not only that GenAI is deeply unreliable, but also that it costs a truly staggering amount of money to operate (OpenAI are burning something like $10 billion a year). What’s the point in replacing an employee you pay $10 an hour to handle customer service issues with a bot that costs $5 for every reply it generates?
Yeah we are on the precipice of a massive bubble about to burst because, like the dot com bubble magic promises are being made by and to people who don’t understand the tech as if it is some magic that will net incredible profits just by pursuing it. LLMs have great applications in specific things, but they are being thrown in every direction to see where they will stick and the magic payoff will come
The problem is that even the specific things they’re good at, they don’t do well enough to justify spending actual money on. And when I say “actual money”, I’m not talking about the hilariously discounted prices AI companies are offering in an effort to capture an audience.
A bot that can do a job reasonably well, but still needs a human to check their work is, from an employment perspective, still an employee, just now with some very expensive helper software. And because of the inherent unreliability of LLMs, a problem that many top figures in the industry are finally admitting may never be solved, they will always need a human to check their work. And that human has to be competent enough to do the job without the AI, in order to figure out where and how it went wrong.
GenAI was supposed to put us all out of work, and maybe one day it will, but the current state of the technology isn’t remotely close to being good enough to do that. It turns out that while bots can very effectively look and sound like humans, they’re not remotely capable of thinking like humans, and that actually matters when your chatbot starts promising customers discounts that don’t actually exist, to name one real example. What was treated as being the last ten percent is actually looking more and more like ninety-nine percent of the work in terms of creating something that can effectively replace a human being.
(As an aside, I can’t help but feel that a big part of this epic faceplant arises from Silicon Valley fully ingesting the bullshit notion of “unskilled labour”. Turns out working the drive thru at McDonald’s is a more complicated job than people think, including McDonald’s themselves. We’ve so undervalued the skills of vast swathes of our population that we were easily deluded into thinking they could all be replaced by simple machines. While some of those tasks certainly can, and will, be automated, there are some human elements - especially in conflict resolution - that are really hard to replace)
Yea, the “cheaper than droids” line in Andor feels strangely prescient ATM.
What are the chances that this is the investors getting cold feet about the AI hype?
Investors have proven over and over they’re credulous idiots who understand sweet fuck-all about technology and will throw money at whatever’s in their face. Creepy Sam and the Microshits will trot out some more useless garbage and prize a few more billion out of the market in just a little while.
NVIDIA has been having a lot of problems with their 13th/14th gen CPU’s degrading. They are also embroiled in an anti-trust investigation. That coupled with the “growing pains of generative AI” has caused them a lot of problems where 2 months ago they were one of the world’s most valuable companies.
Some of it is likely the die-off of the AI hype but their problems are farther reaching than the sudden AI boom.
Thanks!
I would expect “faster” to be a way
or cheaper
“I can easily do it on my phone” is also good.
The important thing here isn’t that the AI is worse than humans. It’s than the AI is worth comparing to humans. Humans stay the same while software can quickly improve by orders of magnitude.
LLMs as they stand are already approaching the improvement flatline portion of the sigma curve due to marginal data requirements increasing exponentially.
It’s a known problem in the actual AI research field that nobody in private industry likes to talk about.
If it scores 40% this year it’ll marginally increase by 10% next year then 5% 3 years later and so on.
AI doesn’t follow Moore’s law.
So far “more data” has been the solution to most problems, but I don’t think we’re close to the limit of how much useful information can be learned from the data even if we’re close to the limit of how much data is available. Look at the AIs that can’t draw hands. There are already many pictures of hands from every angle in their training data. Maybe just having ten times as many pictures of hands would solve the problem, but I’m confident that if that was not possible then doing more with the existing pictures would also work.* Algorithm design just needs some time to catch up.
*I know that the data that is running out is text data. This is just an analogy.
And at rather ridiculously fast paces, as demonstrated by comparing the different versions of Midjourney
The difference in being able to generate realistic humans is even more striking.The question is where do the current LLMs fit in that kind of a timeline.
Theoretically that’s true. Can you tell techbros and the media to shut up about AI until it happens though?
Shut up about it ; )
The AI we have today is the worst it’ll ever be. I can only think of two possible scenarios where AI doesn’t eventually surpass human on every single cognitive task:
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There’s something fundamentally different about computer made of meat (our brains) that cannot be replicated in silica. I personally don’t see this as very likely since both are made of matter and matter obeys the laws of physics.
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We destroy ourselves before we reach AGI.
Otherwise we’ll keep improving our technology and inching forward. It may take 5 years or 50 but it wont stop unless either of the scenarios stated above is true.
LLMs are fundamentally a dead end though. If we ever create AGI, it will be a qualitatively different thing from an LLM.
It’s not obvious to me as to why this is for 100% certainty going to be the case. Even if it’s likely true, there’s still a chance it might not be.
Zero chance IBMs most likely word predictor will become anything more than what it is programmed to be. It is not magic, witches dont exist.
So it is so because you say it’s so? Okay. I remain unconvinced.
People were being shown deus ex machina in supposedly sci-fi movies and series for many years.
Only there it was always meant as 1 in a billion event, as a miracle.
Here a lot of people want to streamline miracles, while even one hasn’t been produced yet.
It’s the difference between Tolkien’s dwarves and Disney’s gnomes.
It would be odd if AI somehow got worse. I mean, wouldn’t they just revert to a backup?
Anyway, I think (1) is extremely unlikely but I would add (3) the existing algorithms are fundamentally insufficient for AGI no matter how much they’re scaled up. A breakthrough is necessary which may not happen for a long time.
I think (3) is true but I also thought that the existing algorithms were fundamentally insufficient for getting to where we are now, and I was wrong. It turns out that they did just need to be scaled up…
It would be odd if AI somehow got worse.
No its not odd at all, its the opposite, it is happening and multiple studies are showing its decay is being caused by feedback entropy which is a real problem to remove
Multiple studies are showing that training on data contaminated with LLM output makes LLMs worse, but there’s no inherent reason why LLMs must be trained on this data. As you say, people are aware of it and they’re going to be avoiding it. At the very least, they will compare the newly trained LLM to their best existing one and if the new one is worse, they won’t switch over. The era of being able to download the entire internet (so to speak) is over but this means that AI will be getting better more slowly, not that it will be getting worse.
It’s possible that the way of generative AI and LLMs is a dead end but that wouldn’t be a stop, only a speed bump. It would only mean it takes longer for us to get there, not that we wouldn’t get there.
I don’t disagree, but before the recent breakthroughs I would have said that AI is like fusion power in the sense that it has been 50 years away for 50 years. If the current approach doesn’t get us there, who knows how long it will take to discover one that does?
Right and all the dogs in the race are now focused on neural networks and llms, which means for now, all the effort could be focused on a dead end. Because of the way capitalism is driving AI research, other avenues of AI research have almost effectively halted, so it will take the current AI bubble to pop before alternative research ramps up again
Like every time there’s an AI bubble. And like every time changes are that in a few years public interest will wane and current generative AI will fade into the background as a technology that everyone uses but nobody cares about, just like machine translation, speech recognition, fuzzy logic, expert systems…
Even when these technologies get better with time (and machine translation certainly got a lot better since the sixties) they fail to recapture their previous levels of excitement and funding.
We currently overcome what popped the last AI bubbles by throwing an absurd amount of resources at the problem. But at some point we’ll have to admit that doubling the USA’s energy consumption for a year to train the next generation of LLMs in hopes of actually turning a profit this time isn’t sustainable.
The issue I have with referring to the current situation as a bubble is that this isn’t just hype. The technology really is amazing, and far better than what people had been expecting. I do think that most current attempts to commercialize it are premature, but there’s such a big first-mover advantage that it makes sense to keep losing money on attempts that are too early in order to succeed as soon as it is possible to do so.
I think that’s intentional. Nation states and other powers that be have working propaganda mechanisms.
A real AGI is a change most important in the sense of power, not in the sense of economy (because we know how to make new humans and educate them, it wouldn’t be a qualitative change there).
All this AI gaslighting is intended to stall real advancements there.
The Web in some sense was produced in the context of AI research. In general semantic and hypertext systems were. And look what it has done to the world. They may just not want another such cataclysm.
EDIT: Also notice the shift from the hypertext paradigm to the application platform paradigm in the Web.
The timeline doesn’t really matter to me personally. As long as we accept the fact that we’ll get there sooner or later it should motivate us to start thinking about the implications that comes with. Otherwise it’s like knowing there’s an asteroid hurling towards the earth but we’ll just dismiss it by saying: “Eh, it’s still 100 years away, there’s no rush here”
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This is an old study, they tested University level adults against the standard Llama2-70B.
Kinda absolete now, the model has completely fallen out of use, for the newer and far better 3 and 3.1 Versions. It also wasnt fine tuned for summarization, and while base L2-70B was OK, it wasnt great at anything without fine tuning.
This clickbait title also sounds like self gratification, the abysmal reading comprehension in the Internet is directly counter to it. The average human found on the Internet doesnt approch the level of literary capabilities, that those ten human testers showed in the study.
As someone who regularly uses AI for my podcast, it’s absolutely worse in every way, but it does 95% of the work. Creates chapters, makes subtitles and a summary, and then I come and just clean up the rest. If it weren’t for AI I just wouldn’t bother with it.
I think this is the thing a lot of people don’t understand about AI; it’s not going to replace humans entirely, but it can make a human way more efficient, and make 1 human able to do the work of 3-5 humans.
I’m doing a series of conversations/interviews with my parents’ generation to keep a voice record of their stories. As part of that, I’m doing transcripts that start with the transcript feature of Google’s Recorder. It can do some nifty things like assign speakers to individual voices. I have to clean up the transcripts some, but it’s far less laborious than dealing with a 15-20 minute conversation. I can fix up a transcript in maybe 5 minutes.
Exactly
but it can make a human way more efficient, and make 1 human able to do the work of 3-5 humans.
Not if you have to proof-read everything to spot the entirely convincing-looking but completely inaccurate parts, is the problem the article cites.
You think it’s not faster to read and correct than it is to write the whole thing?
If the error is hidden well, yes. Close-reading a text and cross referencing everything it says takes MUCH longer than writing a piece you know is accurate to begin with
Absolutely incorrect.
For summarization, having the data correct is crucial because manual typing itself is not a large chore. AI tends to shine more when you’re producing a lot of manual labor such as a 10-page document for something. At that point, the balance tips the other way where proofing and correcting is much easier and less time-consuming than the production itself. That’s where AI comes in for the gains in workflows. It has other fantastic uses as well, like being another voice for brainstorming ideas. If done well, you’re not taking the AI’s idea so much as just using it to spur more creative thinking on your end.
This reminds me. What happened to that tldr bot? I did appreciate the summaries, even if they weren’t perfect.
Meanwhile, here’s an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn’t exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:
Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It’s also worth noting that Claude 3 Opus doesn’t have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).
My guess ist that even if it would be better when it comes to generic text, most of the texts which really mean something have a lot of context around them which a model will know nothing about and thus will not know what is important to the people working with this topic and what is not.
“AI” or Large Language Models, are designed by definition to give averaged answers. So they’re not just averaging on the text you give them, they’re averaging it with all general text of the training model, to create a probabilistically average result based on all of it.
There’s no way around this, because it’s simply how such systems work. It’s their lifeblood to produce a “best guess” across large amounts of training data …which is done by averaging out all that language. A large amount of language… Hence the name.
Are we talking 10% worse and 95% cheaper? Or 50% worse and 10% cheaper? Or 90% worse and 95% cheaper?
Because that last one is good enough for fiscal conservatives. Hell, the second one is good enough for fiscal conservatives.
The linked pdf lists the deficiencies of the LLM responses. They are varied and it sometimes misses the mark completely or cant grasp vital context.
Still pretty useless comparison, they testet 10 university level humans against Llama2-70B. The model has fallen out of use completely by now and was never really great at summarization. The study didnt fine tune it either, so this isnt really representative of the current situation.
There are far better models out, that were either especially trained for summarization or can be easily fine tuned to excel at it. Not to mention the Llama3 and 3.1 series, with the crazy 405B model.
Knowing this it seems like a very low quality study. They should probably redo this with multiple conditions.
- Base Llama 3
- Tuned Llama 3
- Untrained human summarizer
- trained/professional human summarizer
There are far better models out
I’ve heard this refrain a few times. Still waiting for it to pan out.
The next update will fix everything, just need this one hotfix and everything will be solved, just wait.
Just one more update, okay? Just one more. One update. Just one.
