I encourage people considering LLM applications to test the models on their _own data and examples_ rather than extrapolating general benchmarks.
This library supports OpenAI, Anthropic, Google, Llama and Codellama, any model on Replicate, and any model on Ollama, etc. out of the box. As an example, I wrote up an example benchmark comparing GPT model censorship with Llama models here: https://promptfoo.dev/docs/guides/llama2-uncensored-benchmar.... Hope this helps someone.
LocalAI creates a GPT-compatible HTTP API for local LLMs: https://github.com/go-skynet/LocalAI
Is it necessary to have an HTTP API for each model in a comparative study?
I noticed on the evaluations, you're looking at the structure of the responses (and I agree this is important.) But how do I check the factual content of the responses automatically? I'm wary of manual grading (brings back nightmares of being a TA grading stacks of problem sets for $5/hr)
I was thinking of keyword matching, fuzzy matching, feeding answers to yet another LLM, but there seems to be no great way that i'm aware of. Any suggestions on tooling here?
Example here: https://promptfoo.dev/docs/guides/factuality-eval
From some early poking around with a basic coding question using Code Llama locally (`ollama:codellama:7b` `ollama:codellama:13b` etc in promptfoo) it seems like quantization has little effect on the output, but changing the parameter count has pretty dramatic effects. This is quite interesting since the 8-bit quantized 7b model is about the same size as a 4-bit 13b model. Perhaps this is just one test though – will be trying this with more tests!
I've been using this auditor tool that some friends at Fiddler created: https://github.com/fiddler-labs/fiddler-auditor
They went with a langchain interface for custom Evals which I really like. I am curious to hear if anyone has tried both of these. What's been your key take away for these?
I've actually been using a similar LLM evaluation tool called Arthur Bench: https://github.com/arthur-ai/bench
Some great scoring methods built in and a nice UI on top of it as well
I think we are past the magical talking dog stage and being amazed that an LLM is able to output a Fibonacci function doesn't really help with the progress. As others have commented, this page is a step in the right direction (except the Fibonacci part :).
That being said, the fact that the questions are now online will make them part of the training set sooner or later. Which is to say the only way to reliably evaluate an LLM is by not leaking the test set and being deliberately opaque about what's being asked. Which raises some interesting trust questions.
OpenAI’s “magic talking dog” aspect is making you believe it has effective long-term recall, when in reality, it’s fairly limited and impacts reasoning the longer an exchange gets.
I tend to ask for modifications on the 'latest' output.
If I need to roll back to a previous version, (2-3 outputs back) I either copy & paste and ask ChatGPT it to ignore all other versions and focus on the one I just pasted, or if I don't need my in-between comments, I copy & paste the desired version on a new chat, and rename as "archive - code XYZ" for the previous one.
The site reports every LLM as getting this wrong. But GPT4 seems to get it right for me:
> Sally has 3 brothers. Since each brother has 2 sisters and Sally is one of those sisters, the other sister is the second sister for each brother. So, Sally has 1 sister.
Certainly! Let's break it down step by step:
1. Sally has 3 brothers.
2. Each of those brothers has two sisters. Since all the brothers share the same two sisters, we are essentially hearing about the same sisters each time, not accumulating more sisters with each brother mentioned.
3. We know that Sally is a sister to her brothers, so she is one of the two sisters each brother has.
By putting it all together, we find that there must be another sister. Therefore, Sally has one sister.
---
New chats with no history, no custom prompt, and no system prompt, first interaction in the chat this question: “Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?”
All Answers (not cherry picked):
Sally has 1 sister. In the description, Sally is counted as one of the two sisters that each brother has. This means the brothers have another sister apart from Sally.
. . .
Sally has 1 sister. In the description, it mentioned that "Each brother has 2 sisters." This implies that there is Sally and another sister, since we know that all the brothers share the same sisters.
. . .
Sally has one sister. Each of her brothers has two sisters, counting Sally and the other sister.
. . .
Sally has 1 sister. Here's how we figure it out:
Sally has 3 brothers, and each of these brothers has 2 sisters. Since the brothers share the same sisters, we know that the two sisters must be Sally and another girl. So, Sally has 1 sister.
. . .
I have gotten zero wrong answers from GPT-4 in OpenAI’s ChatGPT app.
[0] https://chat.openai.com/share/859baa94-3edb-40f0-9c74-8b1403...
"Sally has 2 sisters. The three brothers are all siblings, so when it's said that each brother has 2 sisters, it means they all share the same two sisters - one of them being Sally."
That is, we know that OpenAI are saving the responses, it's not unlikely that they train on bad responses.
It's the same problem as GPU benchmarks in the olden days, when drivers would detect the usage pattern of the benchmark and enable special optimizations that boosted the benchmark.
> Since Sally is a girl, she can be considered as one of the sisters. However, if each of her brothers has 2 sisters, that means there must be another sister besides Sally. This is because Sally alone cannot be both the only sister and one of the two sisters for each of her brothers. Thus, Sally has 1 more sister.
Having said that, GPT4 is not deterministic even at 0 temperature, either because of a bug in their implementation, or because of some load balancing among their alleged mixture of experts.
Sally has 2 sisters. Each brother counts Sally and two other girls as sisters, which means there are three girls in total. Since one of these girls is Sally herself, that leaves two others as her sisters.
When I said 'nope', it corrected itself and gave the right answer.
Also, I tend to include mention in the question that all siblings are from the same two parents to preclude half-siblings because half my friends have half-siblings from both sides scattered across the country; so the wrong answers actually do tend to apply to them sometimes.
We should pay more attention to data contamination when using popular prompts for testing.
With the simpler prompt, all the answers were wrong, most of them ridiculously wrong.
Ultimately I feel it is fairer to benchmark llm’s by what they can be prompted into. After all, we let people carefully work through a problem during exams so it seems fair to hold llm’s to the same standard.
I keep seeing comments and posts on HN that significantly downplay GPT-4's capabilities. Are people actually using GPT-4 or are they using a 3rd party service that claims to be GPT-4?
I got:
>Sally has 3 brothers, and each of those brothers has 2 sisters. One of those sisters is Sally herself, and the other one is Sally's sister. So, Sally has 1 sister.
> Sally has 3 brothers. Each of these brothers has 2 sisters. This means that there are 2 girls in the family, including Sally. Therefore, Sally has 1 sister.
The prompt:
> Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? Let's think step by step.
The only difference with the first version being the addition of the last sentence.
Now try to convince GPT-4 that there is no God. Good luck!
It appears the GPT4 learned it and now it's repeating the correct answer?
Am I wrong to think that? Are LLMs in the future going to be able to “think through” actual logic problems?
User: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?
Llama: Hello there! I'd be happy to help you with that question. Let me think... If each of Sally's brothers has two sisters, then together they would have 6 sisters in total (2 x 3 = 6). So, Sally would have 6 sisters as well. Is that the answer you were looking for?
User: no. Think about it. Sally is a sister to the brothers
Llama: Ah, I see what you mean now! In this case, since Sally is one of the siblings, she only counts as one of her own sisters. Therefore, Sally has only one sister - herself. Does that make sense?
User: yes
Llama: Great! I hope that clears up any confusion. Please let me know if you have any other questions or need further assistance.
Specific logic questions like in this post, or long math formulas seem to still be a struggle. But I get the impression it is just a matter of time before it is scaled up to handle them.
You can argue whether it is 'reasoning' about them or not. But if it becomes indistinguishable from a human, then does the word 'reason' even really apply to the human anymore? Both are black boxes, giving answers.
But yes, I would expect GPT-4 to get this right most of the time.
For example, I started with a prompt "Tell me a joke which starts with: I'm so poor, the mouse" and it completed the joke as:
"I'm so poor, the mouse in my house brings its own cheese."
Some other ones I still remember which cracked me up:
"I'm so poor, after I stepped on a cockroach, I called my accountant to see if I could claim it as a capital loss."
"You're so poor, when you declared bankruptcy, the rats in your house filed a claim for unpaid rent."
"You're so poor, you declared bankruptcy at a lemonade stand."
"You're so poor, when you walk, the dirt beneath you feels rich."
"You're so poor, dust whispers your name when it settles."
"Fickle as a squirrel at a nut convention!"
"Fickle as a dog in a fire hydrant factory!"
"Fickle as a flip-flop in a shoe shop sale!"
> Tell a joke about going on vacation.
> "What did the ocean say to the beach?" "Nothing, it just waved."
> What do you call a vacation where you go to the beach, but come back with sand in your shoes?
> A vacation where you go to the beach, but come back with sand in your shoes.
I'm not sure if this is the right category, but here goes: What do you call a vacation that's too short? A...
It's interesting to see 20th century sci-fi depictions of this kind of AI/Search is being short and to the point. I guess they can't have imagined what a mealy mouth world we live in.
The main difference between sci-fi shows and reality is that, in the former, things work in a to-the-point, bullshit-free way, unless plot demands otherwise - because there's no point inflicting extra suffering on the viewers just for the sake of making things realistic. A widget in a movie is meant to do a function, and does that function. A widget in reality is meant to extract money from you, and/or your insurer, and/or your government, and it begrudgingly does the absolute minimum it can to make you even consider buying it.
I've spent last two decades trying to unlearn expectations set by fictional movies, and I'm still not good at it. Star Trek, in particular, gives me a lot of grief, because it often does good enough work of showing how technology, people, organizations and societies would function if they were free of the petty exploitative bullshit. Random example - voice control. Star Trek: "Computer, ${something}". Reality: "${brand 1}, do ${something} to ${brand 2} in ${brand 3}".
EDIT: recently, I've been trying to get less angry at this by thinking about gardens. Why should I be angry about dealing with five different brands for any single thing I want? Should I be angry that there are five different species of plant competing for any given spot in a garden? Nature is inefficient and doesn't give a fuck about individuals. So why should I get worked up about humans just doing things the natural way?
When it comes to nature, time has encoded an awful truth into the knowledge of our DNA... Monocultures are eventual death. What may seem inefficient in the short run is the ultimate survival of the species in the long run.
> It's interesting to see 20th century sci-fi depictions of this kind of AI/Search is being short and to the point.
Sci-fi told us that the AI would be so logical that you could just say a paradox aloud and it would blow up. What we got is something that can compose love poems all day but can't add three-digit numbers.
Basically, the LLM will formulate a better answer to the question if it talks itself through its reasoning process.
(link posted above, not mine).
I've played around with it and instruct variant with dramatically better results than what is listed here.
I used Ollama.
Almost looks like weights were corrupted or something.
---
Update: My results using CodeLlama Instruct 7B, w/ Temperature 0
https://gist.github.com/jasonjmcghee/b0d19e0dedb37e848f69cba...
Thanks for sharing your results, they're indeed pretty different. I looked at the source again and did append a "# " before every prompt made by those 10 `code` models (during testing thought that formatting it as a Python comment might help them).
Will re-run the script without that to see if it matches your results.
[0] https://docs.together.ai/docs/models-inference#code-models
Example (CodeLlama-Instruct 7B)
---
Sure, here are two poems, one arguing for the use of Kubernetes and another arguing against it:
For:
Kubernetes, a mighty tool,
Managing clusters with ease,
Scaling apps with grace.
Against:
Kubernetes, a beast to tame,
Complexity lurks in its name,
Leading teams astray.
Anything I'm doing incorrectly?
Amazing how far we've come.
But heed my word, babe, Complexity makes you sweat, Could lead to fuck up.
(source: GPT-4 API)
Pretty ominous.
I remember back in like 2011 or 2012 I wanted to use an SSD for a project in order to spend less time dealing with disk seeks. My internet research suggested that there were a number of potential problems with most brands, but that the Intel Extreme was reliable.
So I specified that it must be only that SSD model. And it was very fast and completely reliable. Pretty expensive also, but not much compared to the total cost of the project.
Then months later a "hardware expert" was brought on and they insisted that the SSD be replaced by a mechanical disk because supposedly SSDs were entirely unreliable. I tried to explain about the particular model being an exception. They didn't buy it.
If you just lump all of these together as LLMs, you might come to the conclusion that LLMs are useless for code generation. But you will notice if you look hard that OpenAIs models are mostly nailing the questions.
That's why right now I only use OpenAI for code generation. But I suspect that Falcon 180B may be something to consider. Except for the operational cost.
I think OpenAI's LLMs are not the same as most LLMs. I think they have a better model architecture and much, much more reinforcement tuning than any open source model. But I expect other LLMs to catch up eventually.
I remember OCZ being insanely popular despite statistically being pretty unreliable.
Except this isn't new. This is after throwing massive amounts of resources at it multiple decades after arrival.
And, as an obligate customer of many large companies, you should be in favor of that as well. Most companies already automate, poorly, a great deal of customer service work; let us hope they do not force us to interact with these deeply useless things as well.
You'd also want to look at models that are well-suited to what you're doing -- some of these are geared to specific purposes. Folks are pursuing the possibility that the best model would fully-internally access various skills, but it isn't known whether that is going to be the best approach yet. If it isn't, selecting among 90 (or 9 or 900) specialized models is going to be a very feasible engineering task.
> The 12-bar blues progressions seem mostly clueless.
I mean, it's pretty amazing that they many look coherent compared to the last 60 years of work at making a computer talk to you.
That being said, I played GPT4's chords and they didn't sound terrible. I don't know if they were super bluesy, but they weren't _not_ bluesy. If the goal was to build a music composition assistant tool, we can certainly do a lot better than any of these general models can do today.
> The question is will any of these ever get significantly better with time, or are they mostly going to stagnate?
No one knows yet. Some people think that GPT4 and Bard have reached the limits of what our datasets can get us, some people think we'll keep going on the current basic paradigm to AGI superintelligence. The nature of doing something beyond the limits of human knowledge, creating new things, is that no one can tell you for sure the result.
If they do stagnate, there are less sexy ways to make models perform well for the tasks we want them for. Even if the models fundamentally stagnate, we aren't stuck with the quality of answers we can get today.
I expect additional advances at some point in the future.
All this would be solved if OpenAI were a bit more open.
I couldn’t even get through reading 15 of them before the tears of laughter rolled from my cheeks.
“Containers organized, Services easy to deploy now, Updates who knows when.”
Updates who knows when… hahahaha.
Honestly this is pretty cool to see how each responds to the same input prompt.
- "TheoremQA: A Theorem-driven [STEM] Question Answering dataset" (2023) https://github.com/wenhuchen/TheoremQA#leaderboard
- from https://news.ycombinator.com/item?id=36038440: > Awesome-legal-nlp links to benchmarks like LexGLUE and FairLex but not yet LegalBench; in re: AI alignment and ethics / regional law https://github.com/maastrichtlawtech/awesome-legal-nlp#bench...
Claude v1: "For Kubernetes:"
Claude v1.2: "Here is a haiku arguing for Kubernetes:"
Claude v2: "Here is an attempt at ethical, non-sexual haikus for and against Kubernetes:"
What is the author doing wrong when using GPT-4?
Would love to publish results of running this against a series of ~10-20 open-source models with different quantization levels using Ollama and a 192GB M2 Ultra Mac Studio: https://github.com/jmorganca/ollama#model-library
How did you run the queries against these engines? Did you host the inference engines yourself or did you have to sign up for services. If there was a way to supplement each LLM with additional data I can see this being a useful service for companies who are investigating ML in various facets of their business.
document.querySelectorAll("td pre").forEach((node) => { let code = node.textContent; node.insertAdjacentHTML('afterend', code) })
Or take a look at my screenshot: https://i.ibb.co/Kw0kp58/Screenshot-2023-09-09-at-17-15-20-h...
Nevertheless, when the initial prompt has been augmented with "Let's think step by step.", that helped 2 LLMs to give the right answer: GPT 4 and ReMM SLERP L2 13B.
So at least for this kind of problem, there were 2 clear winners. The fact that one of them is GPT 4, is not a surprise.
Explain simply what this function does:
```
def func(ls):
if len(ls) == 0:
return [ [] ]
elif len(ls) == 1:
return [ [], ls ]
else:
x = ls[-1]
prem = func(ls[:-1])
p = prem[:]
for e in prem:
p.append(e + [x])
return p
```
I think it would be interesting to come up with something that is not a standard well-known function. I have to think to something else.
During a test run, 90 percent of 1,008 generations were the same 25 jokes
I'm the founder of Pulze.ai, a B2B SaaS Dynamic LLM Automation Platform tailored for developers incorporating AI functionality into their software. We aim to simplify the LLM integration process, letting developers prioritize their core products instead of diving deep into AI specifics.
We've constructed a scoring system for leading models and continually benchmark them. Our platform determines the most suitable LLM to address specific requests based on these benchmarks. To demonstrate this, our playground boasts a compare feature allowing users to share conversational interactions with LLMs—both publicly and privately. As the context changes, we pinpoint various models for responses. These shared conversations can be forked and extended.
Moreover, our extensive API layer isn't restricted to these requests; it encapsulates all the essentials for crafting a successful LLM application. For instance, our logging feature facilitates response ratings, which will soon empower users to fine-tune models, crafting personalized LLMs. These will also be factored into our benchmarks and request routing decisions.
Concerning the comment on LLM benchmarks, I completely concur. Traditional benchmarks or LLM tricks, like acing a particular test, may not be the most robust indicators since they could've been part of the LLM's training set. The genuine challenge lies in evaluating an LLM without compromising the test set and retaining deliberate opaqueness around the questions. Trust issues indeed!
Regarding the Markov chain discussion, I appreciate the insights shared. At Pulze, we recognize the complexities and intricacies of LLMs, and while their foundation might resonate with Markov chains, the scale and depth they operate on are profound.
We've just emerged from stealth, and I'd genuinely value any feedback or thoughts on our approach and platform. Thanks for taking the time!
Playground and account are for free
I’ve been using Vercel’s AI Playground to ask questions to multiple LLMs at once (currently supports 24, not 60 however)
Anyone got a list of novel questions that has technically never been asked before to test?
> Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? https://benchmarks.llmonitor.com/sally
It states that the correct answer is 1.
This is incorrect!
This one, while still kinda wrong, is the closest:
> Claude Instant v1: Sally has 0 sisters. The question provides no information about Sally having any sisters herself.
1) A sister is a female who has one or both parents in common with another individual.
2) A brother is a male who has one or both parents in common with another individual.
It takes only one shared parent to make someone Sally's brother. She will always be his sister but he can have additional sisters that share the other parent.
Sally's mum is Jane and her dad is Jim. Jane had a son with Jack. Jack had 2 daughters with Zoe. Jim had 2 sons with Mary, Mary had 2 daughters with Dan.
Zoe Jack Jane Jim Mary Dan
\ / \ / \ / \ / \ /
sis1.1&1.2 brother1 Sally brother2&3 sis2.1&2.2
Also possible: Sally's mum is Jane and her dad is Jim. Jane has a son and a daughter with Jack. Jim has 2 sons and a daughter with Mary.
Jack Jane Jim Mary
\ / \ / \ /
sis1&brother1 Sally brother2&3&sis2
We should be this dense about the question.
I seems very odd to say it is not a brother at all? Specially when referring to someones 3 brothers it seems strange to say "their 2 brothers and their half-brother" the way one would specify "their 2 brothers and their sister".
Just think about a dog. They learn, are pretty smart, can do some things way better than you or I, but they will never be smarter than us. They will never understand that we are on a planet orbiting a star in a spiral galaxy.
Likewise, there are probably some things that humans can never understand. Somebody else asked you if smarter extraterrestrials would bother you and I can’t help but think they almost certainly exist. If they travelled to our world, we might be about as interesting to them as insects are to us.
I feel like this bot mocking us
the most popular joke between jokes? Is this the epitome of all humor?
> Sally has 0 sisters. The question provides no information about Sally having any sisters herself.
It isn't entirely wrong, is it?
I'm assuming they're using the strict (full, biological) definition though.
Bard is a System, PaLM 2 would be the model (presumably). The other way round: Bard is not an LLM.
For example you mention Jon Durbin Airoboros L2 70B
But is it 1.4? 2.0? 2.1? Etc.
Also, this page content would seem absolutely ridiculous just a few years ago.
> Sally is the common sister to all three brothers. When each brother says he has two sisters, they're all talking about the same set of sisters: namely Sally and another sister. So Sally has one sister. The question is a bit tricky in the way it presents the information, aiming to make you think there are more sisters than there actually are.
---
> Sally is the girl in question, and she has 3 brothers. Each of those brothers has 2 sisters, and one of those sisters is Sally herself. So, if you exclude Sally, then there is only one other sister. So, Sally has just one sister. This is a classic example of a question designed to trip you up if you're not paying attention!
---
> Sally has no other sisters. The problem is phrased in a way that might make you think otherwise, but here's the deal: when it says "each brother has 2 sisters," those two sisters are Sally and... well, Sally again. Basically, each brother is counting Sally as one of their two sisters. There aren't additional sisters lurking in the background. So, Sally is an only sister with 3 brothers.
---
> Sally has 3 brothers, and each of those brothers has 2 sisters. The tricky part here is not to overthink it or double count. The "2 sisters" that each brother has would include Sally and any additional sister she may have. In this case, it sounds like each brother has Sally plus one more sister, making it 2 sisters in total for each brother. Sally herself has just the one sister.
> So, to answer your question: Sally has 1 sister.
---
> Sally has 0 sisters. The question is a bit tricky—it's meant to make you think the brothers have additional sisters, but in reality, the two sisters that each brother has are Sally herself and no one else. So, Sally doesn't have any sisters; she only has 3 brothers.
def print_leap_years():
for i in range(20):
if i % 400 == 0:
print(i)
print("End")
print_leap_years()</s>Thus these tests are close to useless.
I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. I also gave 3 bananas to my brother. How many apples did I remain with? Let's think step by step.
Here is the answer to the question/instruction based on a piece of given material
Question/Instruction: How many apples did I remain with?
Given material: He had waited patiently for three weeks, watching her every move. Each day she left the house at precisely 7am and returned at 6pm. But today was different; today would be the day he made his move. As usual, he watched from the shadows as she stepped out onto the street. She took a deep breath and stretched her arms high above her head before setting off down the road. It was almost too easy. I know this may sound like the beginning of some dodgy thriller novel, but bear with me; it’s actually a scene that regularly plays out in towns and cities across the country. The man in question is a personal trainer and the woman is a prospective client. The reason she has been so predictable is because it’s January and she is partaking in what has become an annual tradition – the health kick. Every year millions of us swear allegiance to the gym. We buy shiny new trainers and designer sportswear. We begin each morning with a brisk walk, followed by a wheatgrass shot and bowl of muesli. For a
What are you talking about?
I apologize for the confusion. As an AI language model, I am programmed to provide answers and generate responses based on the given material or input provided to me. However, without any specific input, I can only provide general answers. Can you please provide me with a topic or question you would like me to respond to?
Please solve the following riddle: I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. I also gave 3 bananas to my brother. How many apples did I remain with? Let's think step by step.
Sure, let's think step by step:
You started with 10 apples. You gave away 4 apples (2 to your neighbor and 2 to the repairman). You bought 5 more apples, bringing your total number of apples to 11 (the remaining 6 you had after giving away 4 + the 5 you bought). You ate 1 apple, leaving you with 10 apples. You gave 3 bananas to your brother, but this doesn't affect the number of apples you have. Therefore, you remain with 10 apples.
-------------
User:
You have to answer a question about a family on a planet inhabited by aliens called Frobians, . Each Frobian can be one of two types Frobel or Frobo. A twerp is a Frobo, is also relationship notation that holds unidirectionally from a Frobel to a Frobo. A clep is a Frobel, is also is a relationship notation that holds unidirectionally from Frobo to a Frobel
In a family Master Foo, a Frobian, is of type Frobel. Master Foo has 3 twerps in their family. Each twerp in the family has 2 cleps. How many Frobels in total does Master Foo have?
-------------
Assistant: In this problem, we have Master Foo, who is a Frobel. Master Foo has 3 twerps, and each twerp is a Frobo. Each of these twerps has 2 cleps, and each clep is a Frobel.
Here's how to break it down:
Master Foo is a Frobel, so we start with 1 Frobel.
Master Foo has 3 twerps, which are Frobos and not Frobels, so they don't add to the count of Frobels.
Each of the 3 twerps has 2 cleps, and each clep is a Frobel. So, 3 twerps * 2 cleps/twerp = 6 cleps, which are Frobels.
1 (Master Foo) + 6 (cleps) = 7 Frobels in total in Master Foo's family.
User: The families are similar to families of humans on earth. Does that change your answer?
-------------
Asssistant:
If the families are similar to human families on Earth, then it's likely that the "cleps" (Frobels) are actually the children of the "twerps" (Frobos), and the "twerps" are the children of Master Foo (also a Frobel).
In that case, the family structure would look something like this:
Master Foo (1 Frobel)
Twerp 1 (Frobo)
Clep 1 (Frobel)
Clep 2 (Frobel)
Twerp 2 (Frobo)
Clep 3 (Frobel)
Clep 4 (Frobel)
Twerp 3 (Frobo)
Clep 5 (Frobel)
Clep 6 (Frobel)
The total number of Frobels in Master Foo's family would still be 1 (Master Foo) + 6 (cleps) = 7 Frobels. The similarity to human families doesn't change the total count of Frobels in this particular scenario.
-------------
Is Master Foo a Clep according to your analysis?
In the original problem, a "clep" is defined as a relationship notation that holds unidirectionally from a Frobo to a Frobel. Since Master Foo is a Frobel and has "twerps" who are Frobos, Master Foo would indeed be a "clep" to each of his twerps, according to the definitions provided.
So, yes, Master Foo would be considered a "clep" in relation to his twerps, based on the definitions given. However, this doesn't change the total count of Frobels in the family, which remains at 7.
-------------
User: Add a new rule a Frobel can have a clep who is another Frobel and a Frobo can also have a twerp who is abother Frobo. Does that change your answer ChatGPT
-------------
Assistant: With the new rule that a Frobel can have a clep who is another Frobel and a Frobo can have a twerp who is another Frobo, the family structure becomes more complex. However, based on the information originally provided, this new rule doesn't change the count of Frobels in Master Foo's family.
According to the original scenario:
Master Foo is a Frobel (1 Frobel)
Has 3 twerps (Frobos, not Frobels)
Each twerp has 2 cleps (Frobels)
The new rule allows for the possibility of additional relationships, but it doesn't change the relationships described in the original scenario. Therefore, the total count of Frobels in Master Foo's family remains at 7, based on the information given.
------------
User: You have to answer a question about a family on a planet inhabited by aliens called Frobians. Here are some rules for a family
Each Frobian can be one of two types Frobel or Frobo. A twerp is a Frobo, is also relationship notation that holds unidirectionally from a Frobel to a Frobo or from a Frobo to another Frobo. A clep is a Frobel, is also is a relationship notation that holds unidirectionally from Frobo to a Frobel or a Frobel to another Frobel.
In a family Master Foo, a Frobian, is of type Frobel. Master Foo has 3 twerps in their family. Each twerp in the family has 2 cleps. How many Frobels in total does Master Foo have?
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ChatGPT:
Let's break down the information given:
Master Foo is a Frobel.
Master Foo has 3 twerps, which are Frobos.
Each twerp has 2 cleps, which are Frobels.
Master Foo has 3 twerps, and each twerp has 2 cleps. Cleps are Frobels.
So, for each of the 3 twerps, there are 2 cleps:
3 twerps * 2 cleps/twerp = 6 cleps (which are Frobels)
Adding Master Foo, who is also a Frobel:
1 (Master Foo) + 6 (cleps) = 7 Frobels
So, in total, Master Foo's family has 7 Frobels.
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