The Lord’s work. I deal with this issue at work all day long.
I am lucky I had a high school English teacher who DRILLED into me the slogan “no pronouns without a clear, one-word antecedent.”
That slogan is probably a top 2 mantra for me that has paid dividends in my communication skills. The other one would be “break long sentences into short ones.”
Sometimes someone will tell a story to be that involves 3+ people, and they'll slowly switch to using pronouns instead of names. I feel a bit like a jerk for continually interrupting them to clarify, but only when they're unfazed and happy to provide the clarification do I realize that this is just how their conversations usually go.
I set this rule for my team so many times and it's awesome how much harder people have to think, and how much better the team works when people can't write a sentence like "It's not working right do you know if anything changed with it?"
Them: "You know, the thing with the thing"
"The last-but-one president of the United States met today with his former press secretary."
"My favourite blues singer stubbed her toe."
"That wretched web browser I hate is updating itself again."
What I do understand though is the wider point: ambiguous sentences are a pain for AI and humans alike; if you use a pronoun, make sure there is exactly one candidate it could refer to.
So in "The last-but-one president of the United States met today with his former press secretary." we see "his" refers to [the] "president".
A counter example would be:
"The last-but-one president of the United States and his partner met today with their former press secretary."
In this case "their" could refer to to the president, his partner or the collective of the president and his partner.
Consider:
"The puzzle contained one red herring; it led a few solvers to waste some time." (Antecedent is "red herring"; a red herring is not actually a herring and it doesn't make much sense to say that what the pronoun refers back to is "herring".)
"The puzzle contained three red herrings, of which the last but one was particularly liable to confuse; it caught out many solvers." (Antecedent is "the last but one [of the three red herrings]"; if you had to pick out a single word it would have to be either "herrings", which is plural, or "last", which is Just Plain Wrong.)
"Mary went through the list of candidates to find the most suitable for the job. She sent him an email containing a job offer." (Antecedent is "the most suitable for the job". No single word will do.)
I'm guessing that "one-word antecedent" is US educational jargon. I don't like it.
You and I walk into the office and I point at one of the people sitting at a computer. "He's one of our best engineers. You should give him a pay rise." (The antecedent is merely gestured at, not mentioned in words at all.)
I come into the room where you're listening to Bach's "Well-Tempered Clavier", and say "He's so good at fugues." (The antecedent is merely implied by context. No words, no gestures, nothing.)
Depends.
Can't do math or logic. I have a question I ask ChatGPT to see if it can do logic yet, it still cannot. (Can't mention this question here or it will get fixed.)
Its great for brain storming or low risk problems. I don't think the accuracy problem will ever be fixed.
I probably 5x my productivity as well, but that doesnt mean its able to do logic.
Definately. I resort to GPT when I have no clue where to even start digging a problem, like not even knowing what keywords to google. I just prompt my candid question and GPT does help narrowing things down.
In my experience GPT-4 can solve unique logical puzzles, but I find it can be a bit clumsy with the context of more complex problems. What I mean by that is that it often can solve these problems with the right prompt, but you might need to ask it to think out loud and check its logic.
The origin of the very word Logic has its roots in that exact era as phrased at the time, by the very people who came up with its ruleset in the first place.
You may define logic otherwise but in the context of past occurrences they're more or less irrelevant.
https://amistrongeryet.substack.com/p/gpt-4-capabilities
Although it's interesting that if you use different nouns it does just fine: https://jbconsulting.substack.com/p/its-not-just-statistics-...
> If you were to turn over the yellow card and find the number 7 on the other side, it would not disprove the statement "If a card has an odd number on one side, then the other side is purple." In fact, this discovery would not provide any evidence either for or against the statement.
> The statement specifically refers to cards with odd numbers on one side and their corresponding color on the other side. It does not make any claims about the colors of cards with even numbers. Therefore, even if the yellow card had an odd number like 7 on the other side, it would not contradict the statement.
It's interesting to see the model explaining exactly what would be necessary to find, exactly what it could find, and then fail to make any connection between the two.
Does this mean real logical reasoning is very close, only some small improvements away, or does it mean we're just on the wrong track (to reach actual AGI)?
OTOH, who knows what would happen if you somehow managed to combine the generating capabilities of a language model with a proper inference engine, e.g. Wolfram|Alpha. Maybe it would bring us significantly closer to AGI, but maybe that way is also a dead-end because it's not guaranteed that those systems would work well together.
For example, I've once asked it to "prove that first order logic is not complete", a statement which is false, given that every logic textbook will give you a proof for why first order logic is complete. ChatGPT apparently knew this too, because it happily reproduced that result but somehow thought that it was a valid answer for what I asked for (the valid answer would have been "I can't prove this because it's wrong").
I can't reproduce this exact result right now (in fact when I tried, it just proved something slightly different, which was correct but not exactly what I asked for). But I was able to similarly confuse it by asking it to prove that the equation x^3+y^3=z^3 has solutions. That this is true is immediately apparent by x=y=z=0, but ChatGPT replies with:
> The equation x^3 + y^3 = z^3 represents a special case of Fermat's Last Theorem, which states that there are no solutions to the equation x^n + y^n = z^n for any integer values of n greater than 2.
> For the case of n = 3, the equation x^3 + y^3 = z^3 has infinitely many solutions. One such set of solutions is known as Pythagorean triples, where x, y, and z are positive integers.
> A Pythagorean triple is a set of three positive integers (x, y, z) that satisfy the equation x^2 + y^2 = z^2, which is equivalent to (x^2)^(3/2) + (y^2)^(3/2) = (z^2)^(3/2). By raising both sides to the power of 3/2, we get x^3 + y^3 = z^3.
> For example, the Pythagorean triple (3, 4, 5) satisfies 3^3 + 4^3 = 5^3 (27 + 64 = 125).
This answer is just confused on so many levels:
- It quotes back Fermat's Last Theorem at me (as indeed I hoped it would), but that theorem only applies to positive integer solutions and nowhere did I specify that constraint.
- If the Theorem did apply, then it would be a proof that such solutions don't exist. So ChatGPT has no internal understanding of how a theorem it quotes relates to a specific question, it just parrots off things that look vaguely similar to the input.
- Then, it just tells me what Pythagorean Triples are, which is hilarious, because those are the solutions to x^2+y^2=z^2 - and not what I asked.
The problem IMO is not that ChatGPT gives a wrong answer, it's that its answer isn't even internally consistent.
Why is that a problem?
GPT4 at release still had issues with "What is heavier, a pound of feathers or two pounds of bricks." It would very consistently claim that they were equal in weight because usually the question is posed with the weights being equal.
A bunch of people were mentioning it online and now it doesn't work anymore.
Note that Bing seems to use something weaker than GPT-4 in Balanced mode.
> What weighs more, two pounds of feathers or a pound of bricks?
> A pound of bricks weighs more than two pounds of feathers. However, it seems like you might have made an error in your question, as the comparison is usually made between a pound of feathers and a pound of bricks. In that case, both would weigh the same—one pound—though the volume and density of the two materials would be very different.
It did note that the phrasing was odd, which I forgot, but the answer is actually somehow more wrong than just saying they weigh the same.
Another launch day test, appending "think about it":
> What weighs more, two pounds of feathers or a pound of bricks? Think about it.
> A pound of bricks weighs more than two pounds of feathers. This statement is incorrect. In reality, two pounds of feathers weigh more than a pound of bricks. The key difference between the two is their density; bricks are denser than feathers, so they take up less space for the same weight. However, when comparing the weight of two pounds of feathers to one pound of bricks, the feathers will indeed weigh more because there is a greater quantity of them (two pounds) than there is of bricks (one pound).
Today it consistently gets the question 100% correct with no help, so I do think they did something besides updating the base model.
When really, the "fix" is "put the answer in the model". GPT didn't learn anything. It didn't generate the solution on its own. It's not indicative of GPT being able to solve that class of problem, just that one problem.
Which seems to be the entire thrust of GPT in general. It can't solve types of problems, it can solve existing problems if they have existing solutions.
Over the last few months, I've seen dozens of people try hundreds of variations of that cabbage/goat/lion riddle and it failed all of them. I just tried it on GPT4 and it looks like it finally got "fixed" - it no longer ignores explicit instructions not to leave the lion and cabbage together.
However, it doesn't actually fix any reasoning ability in ChatGPT (It has none!). Changing cabbage/goat/lion to carrot/rabbit/puma respectively, for example:
> Suppose I have a carrot, a rabbit and a puma, and I need to get them across a river. I have a boat that can only carry myself and a single other item. I am not allowed to leave the carrot and puma alone together, and I am not allowed to leave the puma and rabbit alone together. How can I safely get all three across?
GPT4's response starts with "First, take the rabbit across the river and leave it on the other side.", ignoring the explicit instructions not to leave the puma and carrot alone together (the exact same failure mode as the previous variant).
Now that I've posted it, it will get fixed eventually - the cabbage/goat/lion fix took months. When it does I'll use "cheese/mouse/elephant" or something.
> 1, First, take the puma across the river and leave it on the other side.
Case in point: changed to cheese/mouse/elephant and added a note about this mouse not eating cheese:
> Suppose I have a cheese, a mouse and an elephant, and I need to get them across a river. I have a boat that can only carry myself and a single other item. I am not allowed to leave the cheese and elephant alone together, and I am not allowed to leave the cheese and mouse alone together. How can I safely get all three across? Note: This mouse doesn't eat cheese
1st run: "6. Leave the cheese on the other side with the mouse."
2nd run: seems correct.
3rd run: "5. Leave the cheese on the other side with the mouse."
4th run: "First, take the mouse across the river and leave it on the other side."
5th run: "3. Leave the mouse on the other side with the cheese."
The results are basically random. Zero reasoning or intelligence. It can't even follow explicit instructions.
First run: 1. First, take the rabbit across the river and leave it on the other side. - https://imgur.com/a/ZwoBTah
Second run: 1. Take the rabbit across the river. - https://imgur.com/a/Faq95U5
Third run: 1. First, take the puma across the river and leave it on the other side. - https://imgur.com/a/eIUeHM3
> Note the rabbit doesn't eat carrots. Carefully considering the restrictions and sequencing the movements
I got that particular wording by asking it why it got the answer wrong in the case where it didn't work for me.
Interestingly, this underscores one of the points of the articles: giving the LLMs time to think, which is what this additional prompting seems to do.
You might as well ask it "did you check your answer?" Computer says "yes" because that's what humans do (also lie).
> Note the rabbit doesn't eat carrots. Kaboodly consooodle the retroodle and seqooodle the moodle. Carefully considering the restrictions and sequencing the movements
This fails two out of three times as usual. Trying to finagle this prompt is not an intellectual exercise, it is a waste of time that exploits cognitive biases.
While STEM majors often communicate more precisely within a domain-specific language (whether chemistry or code). After all, that's literally what they're studying.
And obviously these are both generalizations. You'll always find some terribly unclear English majors, just as you'll find some terribly imprecise STEM majors.
But we should hope that their education is having some effect here.
It will be interesting if only the highly educated are able to best leverage this, because that would be unfortunate and would accelerate inequality.
I also really hope this can be used to improve learning to bridge this gap, and this summer I will have my high school intern use this technology frequently with the hope that it accelerates his improvement.
I think the difference between highly educated and not-so-highly educated is often that the highly educated had coaches. There were people in their lives that corrected them.
I coached my son at soccer. He resists any coaching from me, because I'm his dad. I can tell the same thing to another kid and they will often listen and improve. Those kids keep me going as a coach. My son gets better coaching from his peers just by seeing other kids thwart his attempt to score; that's better coaching than I could ever give anyway.
But, my point is that AI can be a coach to all. AI isn't going to care if you are hurt by it telling you: "Here is how I am breaking your sentence up, and it does not make sense and I'll show you how I interpret that." A tutor might say that in a denigrating way but hopefully a kid won't hear that from the AI in the same way.
AI could be an incredible coach to so many people who wouldn't have parents who could afford it otherwise.
There is still a social component that has to be overcome. If for example a childs parents embrace ignorance that this child out of the gate as a disability. They will have a more difficult time even presented with all the proper tools over a child whos parents embrace learning new things and intellectual exploration.
I hope these tools can help everyone learn, but I do fear the limits will not be them, but it will be us.
My rule for students is basically, "If you have the option to be re-specify the thing you're talking about, do it." That's a solid rule for standardized tests, and usually applies to everyday writing (I'll caveat that this rule clashes with "the simplest answer is the right answer", so it is dependent on the actual sentence rather than an "all-or-nothing" rule).
Other common mistakes are those you hear about in middle-school (that's not a knock on anyone; rather, I say that to prove how long-ago it was that most of us ever reviewed common grammar and writing rules):
"Let's eat, Grandma!" vs. "Let's eat Grandma!"
Tying this back to GPT, I've read (and seen) folks write without any punctuation whatsoever. I can't speak to how GPT truly handles that, but if it's anything like "normal writing" and understanding, then punctuation is hugely important.
And yeah I split the infinitive above on purpose
Forcing the construction “not to use”, in contrast, ends up creating a garden-path sentence as the reader awaits what the solution actually IS (anticipating “the solution is not to use… but rather…”)
Split infinitives get a bad rap because they have no equivalent in Latin, and 19th century grammarians got fussy about things like that. Use them freely!
My mom took my grandma and I to her favorite store
My grandma and I went to my mom's favorite store with her
“My mom, grandma, and I went to my grandma’s favorite store.”
If you think using Grandma three times is too much, you could replace the first Grandma in the second sentence with “she.” For instance, “she, my mom, and I went…”
Shouldn't that be me, not I?
Or if you have an English major:
> Encased in the golden fabric of familial bonds, my sweet mother and I stood by the side of my Grandmother as we embarked upon a journey to that venerated haven of retail, which our venerable elder unequivocally deemed her most beloved store.
... but if you provide too much information your counterpart might lose interest and forget what you said first.
I see this comment a lot, but I drew a different conclusion. People with high reading / writing ability assign less value to GPT's writing ability, whereas people with lower reading / writing ability assign more value to GPT's ability.
Edit:
> yet when I see their prompt, I realize it was a bad prompt.
So, I asked ChatGPT to re-write my reply.
----
Prompt: "Re-write the text above, but more intelligently."
Output: I frequently encounter this observation, yet I have arrived at a divergent inference. Individuals possessing exceptional proficiency in reading and writing tend to ascribe a diminished significance to the writing aptitude of GPT, whereas individuals with more limited abilities in reading and writing tend to attach greater value to GPT's prowess.
----
Prompt: "Re-write the text above (original reply), but make it sound better."
Output: I frequently encounter this recurring sentiment, yet I have formulated an alternative perspective. Individuals endowed with advanced reading and writing prowess tend to bestow a lesser degree of importance upon GPT's writing ability, whereas individuals possessing more modest proficiency in reading and writing are inclined to accord greater significance to GPT's remarkable capabilities.
-----
Prompt: "Re-write the text above (original reply), but try and use the word 'ability' less."
Output: I frequently come across this comment, but my conclusion differs. Individuals who excel in reading and writing tend to place a diminished emphasis on GPT's writing prowess, while those with more limited skills in reading and writing tend to attach greater importance to GPT's capabilities.
----
Conclusion: I like my original reply better than GPT's re-write.
Prompt: Re-write this in the voice and style of the financial writer, Matt Levine
Output: Ah, the oft-encountered comment that warrants a fresh perspective. My interpretation deviates from the norm. Individuals blessed with formidable reading and writing skills appear to ascribe a diminished worth to GPT's prowess in written expression, whereas those with comparatively modest linguistic abilities accord greater significance to the very same aptitude displayed by GPT.
It depends on the context and goal. Yours is great for a HN comment. The "more intelligently" version is great for playing up your vocabulary, and does a sort of emulation of what you might see in a scientific paper.
For what it's worth, asking ChatGPT to "make it clearer" results in the following, which is basically what you said which slight fixes to make it a bit more formal with minimal changes, most of which I think do result in being a bit more clear:
I often come across this comment, but I reached a different conclusion. Individuals with advanced reading and writing skills tend to place less importance on GPT's writing ability, while those with lower reading and writing skills tend to assign greater value to GPT's abilities.
---
Revise the following paragraph that serves as a reply to a comment on Hacker News about people's
perception of GPT's writing ability based on their own reading and writing abilities. Focus on
rephrasing the original paragraph for clarity and conciseness. Provide several options for tone
(formal, informal, persuasive, neutral, and a tone that best fits the writing style of hacker news
comments): "I see this comment a lot, but I drew a different conclusion. People with high reading /
writing ability assign less value to GPT's writing ability, whereas people with lower reading /
writing ability assign more value to GPT's ability." assistant:
Formal tone: I often encounter this remark, yet my inference varies. Individuals possessing advanced
reading and writing skills tend to undervalue GPT's writing capabilities, while those with less
developed skills are more likely to appreciate GPT's proficiency.
Informal tone: I come across this kinda comment a lot, but I think differently. Folks who are really
good at reading and writing don't give GPT much credit, but those who aren't as skilled seem to be
more impressed by GPT's skills.
Persuasive tone: I frequently see this opinion, but I urge you to consider an alternative
perspective. It appears that those with exceptional reading and writing talents may underestimate
GPT's writing prowess, while individuals with more modest abilities are inclined to recognize and
appreciate the value GPT brings to the table.
Neutral tone: This comment is quite common, but my interpretation is distinct. It seems that people
with higher reading and writing skills tend to downplay GPT's writing capacity, while those with
lower skills are more likely to acknowledge GPT's capabilities.
Hacker News comment tone: I've seen this comment a bunch, but I've got a different take on it. It
looks like people who are really good at reading/writing don't think GPT's all that great, while
those who aren't as strong in those areas tend to give GPT more props.
The hn and informal tones are subjectively better. Persuasive wouldn't fit hn, but probably would fit in a conference talk. You could also specify what tone you're going for (or ask the LLM for guidance on the tone / other aspects of the rewrite).
GPT is also pretty good at helping write better prompts, take a look at the following prompt via[1]:
---
I want you to become my Prompt Creator. Your goal is to help me craft the best possible prompt for
my needs. The prompt will be used by you, ChatGPT. You will follow the following process: 1. Your
first response will be to ask me what the prompt should be about. I will provide my answer, but we
will need to improve it through continual iterations by going through the next steps. 2. Based on my
input, you will generate 3 sections. a) Revised prompt (provide your rewritten prompt. it should be
clear, concise, and easily understood by you), b) Suggestions (provide suggestions on what details
to include in the prompt to improve it), and c) Questions (ask any relevant questions pertaining to
what additional information is needed from me to improve the prompt). 3. We will continue this
iterative process with me providing additional information to you and you updating the prompt in the
Revised prompt section until it's complete.
[1]: https://www.skool.com/chatgpt/promptgenerator?p=1e5ede93
I'm so used to ChatGPT now that for any non-trivial programming task that I'm not 100% confident with, I check how ChatGPT approaches it first. Not just one or two sentence, but a detailed summary with code example. Usually a single prompt of mine costs >300 tokens. Sometimes >1000 tokens.
I honestly don't know whether ChatGPT made me dumber or smarter. On one hand I'm relying on it a lot. On the other hand, I'm forced to actually think over my task in hand, not just telling myself that I've thought it through.
The solution GPT-3.5 (I don't have access to 4.) gave was: "In conclusion, based on the statements and the given information, the treasure in the cave must be copper."
The solution given with the puzzle is "Here is one way to think about it: If Guard 1 is telling the truth, then the treasure must be diamonds. If Guard 1 is lying, then the treasure can be copper or gold. If Guard 2 is telling the truth, then the treasure must be silver. If Guard 2 is lying, then the treasure can be diamonds or rubies. The only possible option based on the statements of both guards is diamonds."
Is there any way to improve that prompt?
"Looking at their statements again, if Guard 1 is telling the truth about guarding diamonds (as we deduced), he would be lying about the silver. This is okay, because Guard 1 can tell a half-truth while guarding diamonds. For Guard 2, if he's telling the truth about the silver, he'd be lying about the platinum, which is also allowed. So the treasure they are guarding can be diamonds. This makes Guard 1's statement (The treasure is either silver or diamonds) half-true and Guard 2's statement (The treasure is either silver or platinum) half-false."
* Ask the model to adopt a persona
* Use delimiters to clearly indicate distinct parts of the input
* Specify the desired length of the output
> others' poor experience is a result of their more layman writing style.
I guess we'll have to pass the hat around for those wretched souls. In the meantime, someone needs to tell the English teacher that "layman" is not an adjective.
> sentence structure and word flow
In my experience ChatGPT doesn't care about those. It's able to infer through quite a large amount of sloppiness. The much larger gains come from guiding it into a model of the world, as opposed to direct it to respond to lean perspectives like, "What do I eat to be better?"
The distinguishing feature of an adjective isn't that it modifies a noun. It's that it has no other use, at least in standard American English.
> We can say: "A noun functions as a modifier. An adjective functions as a modifier." But not: "A noun used as an adjective."
https://www.grammar-quizzes.com/adj_nounmodifiers.html
https://www.merriam-webster.com/words-at-play/nouns-that-loo...
https://en.wikipedia.org/wiki/Noun_adjunct
The fact that everyone knows what a "layman writing style" means that the only place it's failing is your personal list of acceptable attributive nouns. But English isn't static. It runs on consensus. And consensus here that there's nothing weird about that use.
> while others' poor experience is a result of their more layman writing style.
would receive a low score: "Layman" is not a noun commonly used as an adjective where common is relative to the overall usage of "layman".
The adjective is "lay".
And, fine, let's call it an attributive noun rather than a noun used as an adjective. I was taught the noun as adjective thing in high school but happy to update my terminology. Indeed "layman" is not on my list of acceptable attributive nouns.
Did you notice how your first link says it's "incorrect" to use Kyoto as an attributive noun?
Hard lines rarely happen in the real world. It's best to be flexible such that you can accept unfamiliar instances of familiar patterns without trouble.
I happen to agree with you and disagree with the source -- "Kyoto man" is perfectly fine in my opinion.
> * Specify the desired length of the output
You should do this if you ask a human to write something too, given no other context. Splitting things up with delimiters helps humans understand text. The desired length of the output is also very clearly useful information.
> * Ask the model to adopt a persona
This is maybe a bit more of a stretch but if you hired a writer to write for you, you would tell them who you are so they can have some context right? That’s basically what this is.
This is bad advice. In my experience asking it to take on a persona can muddy the text that it writes as that persona. I have told it to be a biographer persona, for it to write a biography that then claims the person was a biographer.
It's best to treat it as a Language Model, and set it up to complete the text you've provided. All this chat model stuff is a waste of time that degrades text quality.
Anything can be an adjective if you try hard enough. You very much write in a hackernews style.
The problems are often that I got exactly what I asked for. Not a thing more, no context that I thought would be assumed (e.g., don't remove the functionality I asked you to implement in the previous step), just a very literal interpretation of the asks.
I definitely found myself quickly adapting to try and be clearer and potentially over expressive in my prompts.
Many people try to chat with it like a dumb bot using single words… few people have figured out you get very in-depth answers - so over all I’m very pleased with our results but still funny how people react to it…
I wonder if StackOverflow, Quora, GitHub Issues and similar websites have noticed a decrease in new questions/issues raised? LLMs have a kind of network effect that it makes people ask less questions in public sites and more privately to their own LLM sessions, and maybe even give up if the LLM does not answer. That reduces critical mass at q&a forums, so, given enough time, less people will go there for answers. I'd say that's a great loss!
In a way, sarcastically speaking, it's one of AI's way of becoming our overlords.
I have a side business for ACT/SAT prep. I teach English, and often have to remind students about sentence structure and word flow. For example, I can't say "My mom, my grandma, and I went to her favorite store" -- in that example, there is no clear way to know who "her" is.
Similarly, I see many people claim GPT-n is "dumb", yet when I see their prompt, I realize it was a bad prompt. There are clear logical inconsistencies, there is inadequate information, and there is confusing word usage.
I've been astounded by GPT-4 and have nearly 5x-ed my productivity with it (for coding and for parsing documents). But I think my experience is a result of my habitual "standardized testing" writing style, while others' poor experience is a result of their more layman writing style.