This is really cool. I can see quite a number of potential applications.
TinyTroupe, a new LLM-powered multiagent persona simulation Python library
31–40 of 55 posts
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#32Earlier quoted context omitted.
I did not test this library so I can't argue from that perspective ( I think I will though ; it does seem interesting ). In a sense, this is what training employees is all about. You want to get them ready for various possible scenarios. For recurring tasks that do require some human input, it does not seem that far fetched. This is the interesting part. Is convincing a bad thing if it does what user would be expecte…
> it will be an engine for simply manufacturing support for a pre-selected option. There's nothing unique about this tool in that regard though. Pretty much anything can be mis-used in that way - spreadsheets, graphics/visualizations, statistical models, etc. etc. Whether tools are actually used to support better decision making, or simply to support pre-selected decisions, is more about the culture of the organizati…
Agreed. At the end of the day, it is just another tool.
I think the issue is the human tendency to just rubber stamp whatever result is given. Not that long ago, few questioned the result of a study and now there won't even be underlying data to go back to see if someone made an error. Naturally, this would suggest that we will start seeing a lot of bad decisions, because human operators did not stop and think whether the response made sense.
That said, I am not sure what can be done about it.
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#33This seems fundamentally unsuitable for its stated purpose, which is “understanding human behavior”. While it may, as it says, produce “convincing interactions”, there is no basis at all peesented for believing it produces an accurate model of human behavior, so using it to “understand human behavior” is at best willful self-deception, and probably, with a little effort at tweaking inputs to produce the desired resul…
I wonder how one could measure the how human-like the agents' opinions and interactions are? There's a ton of value in simulating preferences, but you're right that it's hard to know if the simulation is accurate. I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate.
cw: i don't actually work in ML, i just read a lot. if someone who is a real expert can tell me if my assessment here is correct, please let me know.
> I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate.
That's what an AI model already is.
Let's say you had 10 temperature sensors on a mountain and you logged their data at time T.
If you take the average of those 10 readings, you get a 'wisdom of the crowds' from the temperature sensors, which you can model as an avg + std of your 10 real measurements.
You can then sample 10 new points from the normal distribution defined by that avg + std. Cool for generating new similar data, but it doesn't really tell you anything you didn't already know.
Trying to get 'wisdom of crowds' through repeated querying of the AI model is equivalent to sampling 10 new points at random from your distribution. You'll get values that are like your original distribution of true values (w/ some outliers) but there's probably a better way to get at what you're looking to extract from the model.
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#34Earlier quoted context omitted.
I wonder how one could measure the how human-like the agents' opinions and interactions are? There's a ton of value in simulating preferences, but you're right that it's hard to know if the simulation is accurate. I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate.
cw: i don't actually work in ML, i just read a lot. if someone who is a real expert can tell me if my assessment here is correct, please let me know. > I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate. That's what an AI model already is. Let's say you had 10 temperature sensors on a mountain and you logged their data a…
It's worse than that. LLMs have been tuned carefully to mostly produce output that will be inoffensive in a corporate environment. This isn't an unbiased sampling.
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#35Earlier quoted context omitted.
I did not test this library so I can't argue from that perspective ( I think I will though ; it does seem interesting ). In a sense, this is what training employees is all about. You want to get them ready for various possible scenarios. For recurring tasks that do require some human input, it does not seem that far fetched. This is the interesting part. Is convincing a bad thing if it does what user would be expecte…
> it will be an engine for simply manufacturing support for a pre-selected option. There's nothing unique about this tool in that regard though. Pretty much anything can be mis-used in that way - spreadsheets, graphics/visualizations, statistical models, etc. etc. Whether tools are actually used to support better decision making, or simply to support pre-selected decisions, is more about the culture of the organizati…
> There's nothing unique about this tool in that regard though.
Sure, it’s just part of an arms race where having a new thing with a hot selling pitch to cover that up and put a layer of buzzwords on top of it helps sell the results to audiences who have started to see through the existing ways of doing that.
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#36Earlier quoted context omitted.
cw: i don't actually work in ML, i just read a lot. if someone who is a real expert can tell me if my assessment here is correct, please let me know. > I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate. That's what an AI model already is. Let's say you had 10 temperature sensors on a mountain and you logged their data a…
It's worse than that. LLMs have been tuned carefully to mostly produce output that will be inoffensive in a corporate environment. This isn't an unbiased sampling.
Could be interesting if used with many different llms at once
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#37This seems fundamentally unsuitable for its stated purpose, which is “understanding human behavior”. While it may, as it says, produce “convincing interactions”, there is no basis at all peesented for believing it produces an accurate model of human behavior, so using it to “understand human behavior” is at best willful self-deception, and probably, with a little effort at tweaking inputs to produce the desired resul…
I wonder how one could measure the how human-like the agents' opinions and interactions are? There's a ton of value in simulating preferences, but you're right that it's hard to know if the simulation is accurate. I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate.
Section 6, "Controlled Evaluation," answers that question: https://arxiv.org/pdf/2304.03442
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#38This seems fundamentally unsuitable for its stated purpose, which is “understanding human behavior”. While it may, as it says, produce “convincing interactions”, there is no basis at all peesented for believing it produces an accurate model of human behavior, so using it to “understand human behavior” is at best willful self-deception, and probably, with a little effort at tweaking inputs to produce the desired resul…
The source does not mention the underlying motivation (and it really should), but I think this is it:
https://www.linkedin.com/posts/emollick_kind-of-a-big-deal-a...
"... a new paper shows GPT-4 simulates people well enough to replicate social science experiments with high accuracy.
Note this is done by having the AI prompted to respond to survey questions as a person given random demographic characteristics & surveying thousands of "AI people," and works for studies published after the knowledge cut-off of the AI models."
A couple other posts along similar lines:
https://www.linkedin.com/posts/emollick_this-paper-suggests-...
"... LLMs automatically generate scientific hypotheses, and then test those hypotheses with simulated AI human agents.
https://www.linkedin.com/posts/emollick_formula-for-neat-ai-...
"Applying Asch's conformity experiment to LLMs: they tend to conform with the majority opinion, especially when they are "uncertain." Having a devil's advocate mitigates this effect, just as it does with people."
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#39Actually Sburb.
Re: TinyTroupe, a new LLM-powered multiagent persona simulation Python library
#40Earlier quoted context omitted.
cw: i don't actually work in ML, i just read a lot. if someone who is a real expert can tell me if my assessment here is correct, please let me know. > I have a hunch that, through sampling many AI "opinions," you can arrive at something like the wisdom of the crowd, but again, it's hard to validate. That's what an AI model already is. Let's say you had 10 temperature sensors on a mountain and you logged their data a…
It's worse than that. LLMs have been tuned carefully to mostly produce output that will be inoffensive in a corporate environment. This isn't an unbiased sampling.
True for consumer products like ChatGPT but there are plenty of models that are not censored. https://huggingface.co/models?sort=trending&search=uncensore...