I personally can't take any models from google seriously. I was asking it about the Japanese Heian period and it told me such nonsensical information you would have thought it was a joke or parody. Some highlights were "Native American women warriors rode across the grassy plains of Japan, carrying Yumi" and "A diverse group of warriors, including a woman of European descent wielding a katana, stand together in camar…
Why would you expect these smaller models to do well at knowledge base/Wikipedia replacement tasks? Small models are for reasoning tasks that are not overly dependent on world knowledge.
Gemma: New Open Models
341–350 of 543 posts
Re: Gemma: New Open Models
#342I really don't get why there is this obsession with safe "Responsible Generative AI". I mean it writes some bad words, or bad pics, a human can do that without help as well. The good thing about dangerous knowledge and generative AI is that you're never sure haha, you'd be a fool to ask GPT to make a bomb. I mean it would probably be safe, since it will make up half of the steps.
https://www.theguardian.com/technology/2018/jan/12/google-ra... https://www.bbc.com/news/technology-58462511
Also people are using LLMs to learn (horrifying but reality), it would be unresponsible for them to let it to propagate negative stereotypes and biases.
Re: Gemma: New Open Models
#343Earlier quoted context omitted.
I think you are being biased and closed minded and overly critical. Here are some wonderful examples of it generating images of historical figures: https://twitter.com/stillgray/status/1760187341468270686 This will lead to a better educated more fair populace and better future for all.
Comical. I don't think parody could do better. I'm going to assume given today's political climate, it doesn't do the reverse? i.e. generate a Scandinavian if you ask for famous African kings
There are some great ones in the replies.
I really hope this is just the result of system prompts and they didn't permanently gimp the model with DEI-focused RLHF.
Re: Gemma: New Open Models
#344Earlier quoted context omitted.
Can the Gemma models be downloaded to run locally, like open-source models Llama2, Mistral, etc ? Or is your definition of "open" different?
Their definition of "open" is "not open", i.e. you're only allowed to use Gemma in "non-harmful" way. We all know that Google thinks that saying that 1800s English kings were white is "harmful".
If you know how to make "1800s english kings" show up as white 100% of the time without also making "kings" show up as white 100% of the time, maybe you should apply to Google? Clearly you must have advanced knowledge on how to perfectly remove bias from training distributions if you casually throw stones like this.
Re: Gemma: New Open Models
#345Earlier quoted context omitted.
I was wondering if these models would perform in such a way, given this week's X/twitter storm over Gemini generated images. E.g. https://x.com/debarghya_das/status/1759786243519615169?s=20 https://x.com/MiceynComplex/status/1759833997688107301?s=20 https://x.com/AravSrinivas/status/1759826471655452984?s=20
Regarding the last one: there 1.5 million immigrants in Norway with total population 5.4 million. Gemini isn't very wrong, is it?
Re: Gemma: New Open Models
#346Earlier quoted context omitted.
I find myself shocked that people ask questions of the world from these models, as though pulping every text and its component words relationships and deriving statistical relationships between them should reliably deliver useful information. Don’t get me wrong, I’ve used LLMs and been amazed by their output, but the p-zombie statistical model has no idea what it is saying back to you and the idea that we should trus…
I think you are a bit out of touch with recent advancements in LLMs. Asking ChatGPT questions about the world seems pretty much on par with the results Google (Search) shows me. Sure, it misses things here and there, but so do most primary school teachers. Your argument that this is just a statistical trick sort of gives away that you do not fully accept the usefulness of this new technology. Unless you are trolling,…
But why are these use cases different? It appears to me that code is at least subject to sustained logic which (evidently) translates quite well to LLMs.
And when you ask an LLM to be creative/generative, it’s also pretty amazing - j mean it’s just doing the Pascal’s Marble run enmasse.
But to ask it for something about the world and expect a good and reliable answer? Aren’t we just setting ourselves up for failure if we think this is a fine thing to do at our current point in time? We already have enough trouble with mis- and dis- information. It’s not like asking it about a certain period in Japanese history is getting it to crawl and summarise the Wikipedia page (although I appreciate it would be more than capable of this) I understand the awe some have at the concept of totally personalised and individualised learning on topics, but fuck me dead we are literally asking a system that has had as much of a corpus of humanity’s textual information as possible dumped into it and then asking it to GENERATE responses between things that the associations it holds may be so weak as to reliably produce gibberish, and the person on the other side has no real way of knowing that
Re: Gemma: New Open Models
#347Earlier quoted context omitted.
A caveat: my impression of Phi-2, based on my own use and others’ experiences online, is that these benchmarks do not remotely resemble reality. The model is a paper tiger that is unable to perform almost any real-world task because it’s been fed so heavily with almost exclusively synthetic data targeted towards improving benchmark performance.
Hear hear! I don't understand why it has persistent mindshare, it's not even trained for chat. Meanwhile StableLM 3B runs RAG in my browser, on my iPhone, on my Pixel ..
Re: Gemma: New Open Models
#348I personally can't take any models from google seriously. I was asking it about the Japanese Heian period and it told me such nonsensical information you would have thought it was a joke or parody. Some highlights were "Native American women warriors rode across the grassy plains of Japan, carrying Yumi" and "A diverse group of warriors, including a woman of European descent wielding a katana, stand together in camar…
Were you asking Gemma about this, or Gemini? What were your prompts?
I mean, just asking it for a "samurai" from the period will give you this:
https://g.co/gemini/share/ba324bd98d9b
>A non-binary Indigenous American samurai
It seems to recognize it's mistakes if you confront it though. The more I mess with it the more I get "I'm afraid I can't do that, Dave" responses.
But yea. Seems like if it makes an image, it goes off the rails.
Re: Gemma: New Open Models
#349I personally can't take any models from google seriously. I was asking it about the Japanese Heian period and it told me such nonsensical information you would have thought it was a joke or parody. Some highlights were "Native American women warriors rode across the grassy plains of Japan, carrying Yumi" and "A diverse group of warriors, including a woman of European descent wielding a katana, stand together in camar…
I find myself shocked that people ask questions of the world from these models, as though pulping every text and its component words relationships and deriving statistical relationships between them should reliably deliver useful information. Don’t get me wrong, I’ve used LLMs and been amazed by their output, but the p-zombie statistical model has no idea what it is saying back to you and the idea that we should trus…
I have trouble convincing colleagues (technical people) that the same question is not guaranteed to result in the same answer and there's no rhyme or reason for any divergence from what they were expecting. Imagine relying on the output of an LLM for some important task and then you get a different output that breaks things. What would be in the RCA (root cause analysis)? Would it be "the LLM chose different words and we don't know why"? Not much use in that.
Re: Gemma: New Open Models
#350Earlier quoted context omitted.
I was wondering if these models would perform in such a way, given this week's X/twitter storm over Gemini generated images. E.g. https://x.com/debarghya_das/status/1759786243519615169?s=20 https://x.com/MiceynComplex/status/1759833997688107301?s=20 https://x.com/AravSrinivas/status/1759826471655452984?s=20
Regarding the last one: there 1.5 million immigrants in Norway with total population 5.4 million. Gemini isn't very wrong, is it?