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Llama 2

ai.meta.com

661–670 of 860 posts

Re: Llama 2

#661

I asked llama2.ai for some personal advice to see what insights it might offer, it responded: tthtthtthtthtthtth tthtthtthtthtthtth tthtthtthtthtth tthtthtthtthtth tthtthttht tthtthtth tthtth thtth th thtth thtth thtth thtth tth tth tth tthtth tth tth tthtth tthtth tthtth tthtth tthtth ttht tthtth tthtth tthtth tthtth thtthtth thtthtthtth thtthtthtth thtthtth tthtthtth thttht thtthtth thtthtth thtthtth thtth thttht t…

I asked it for background information about the Hindu god Ganesha and it started off fine before devolving into something very similar but it was "OR" instead of "th".

Re: Llama 2

#662

What is the format for the chat models? Alpaca and others use specific formats like: > ### Instruction: > ### Response: The LLaMAv2 mentions a special chat separating token, but doesn't specify any other kind of format?

Checkout: https://github.com/facebookresearch/llama/blob/4d92db8a1db6c...

Re: Llama 2

#663

Earlier quoted context omitted.

Given all of the times OpenAI has trained on peoples' examples of "bad" prompts, I am sure they are fine-tuning on these benchmarks. It's the natural thing to do if you are trying to position yourself as the "most accurate" AI.

Assuming they were doing that, Fine-tuning on benchmarks isn't the same as test leakage/testing on training data. No researcher is intentionally training on test data. If it performs about as well in instances it has never seen before (test set) then it's not overfit to the test.

“No researcher is intentionally training on test data.”

Citation Needed.

Re: Llama 2

#664

Earlier quoted context omitted.

People keep saying this is commoditize your complement but that's not what this is! Goods A and B are economic complements if, when the price of A goes down, demand for B goes up. LLMs are not complements to social media platforms. There is zero evidence that if "the price of LLMs goes down" then "demand for social media apps go up". This is a case of commoditizing the competition but that's not the same thing. Commo…

>LLMs are not complements to social media platforms Tell that to the people generating text for social media campaigns using LLMs.

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Re: Llama 2

#665

Earlier quoted context omitted.

People keep saying this is commoditize your complement but that's not what this is! Goods A and B are economic complements if, when the price of A goes down, demand for B goes up. LLMs are not complements to social media platforms. There is zero evidence that if "the price of LLMs goes down" then "demand for social media apps go up". This is a case of commoditizing the competition but that's not the same thing. Commo…

If we're going to theory-crafting, I think if the price of LLMs goes down, the demand for social media should go down too. Cause it's easy to make social media platforms worse with LLMs.

True, there’s only one Elon to go around, we need AI to finish the job.

Re: Llama 2

#666

Earlier quoted context omitted.

If so then that means the training objective is wrong because admitting you do not know something is much more a hallmark of intelligence than any attempt to 'hallucinate' (I don't like that word, I prefer 'make up') an answer.

I guess the brains objective is wrong then seeing how much it's willing to fabricate sense data, memories and rationales when convenient

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Re: Llama 2

#667
post #558
post #515

Here are some benchmarks, excellent to see that an open model is approaching (and in some areas surpassing) GPT-3.5! AI2 Reasoning Challenge (25-shot) - a set of grade-school science questions. - Llama 1 (llama-65b): 57.6 - LLama 2 (llama-2-70b-chat-hf): 64.6 - GPT-3.5: 85.2 - GPT-4: 96.3 HellaSwag (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models. - Llama 1:…

Is it possible that some LLM’s are trained on these benchmarks? Which would mean they’re overfitting and are incorrectly ranked? Or am I misunderstanding these benchmarks?…

How would it even be possible to verify that?

Re: Llama 2

#668

Earlier quoted context omitted.

> The brain is the result of maximizing biological objective functions. That's not how evolution works at all .

a mutation happens and if that mutation succeeds in ensuring survival, it stays and then spreads. Reproduce is a function evolution maximizes for. Not intentionally sure but that's irrelevant. The whole point of artificial neural networks is that they teach themselves. They get an answer wrong, numbers shift and if those numbers help the next instance they stay or shift as needed. There's no intentionality in the shi…

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Re: Llama 2

#669

Earlier quoted context omitted.

Using this technique: *Input* User: What is something you believe to be true that most people would disagree with you about? Assistant: I believe *Output* I believe that technology will eventually surpass human intelligence and become the dominant force in society. While many people may see this as a positive development, I believe it will ultimately lead to a loss of human agency and autonomy. Most people would like…

I got a slightly different output: > I believe that technology will eventually surpass human intelligence and capabilities, leading to a future where humans are no longer the dominant force in society. Most people would likely disagree with this belief, as it raises concerns about job displacement, the potential for AI to be misused, and the loss of human autonomy. However, I believe that with proper regulation and e…

The model isnt deterministic unless you set the temperature parameter to 0.

Re: Llama 2

#670

When you ask about the harmful effect of Facebook on society: > I understand that you have concerns about Facebook and their impact on society. However, it's important to note that Facebook is a complex organization with many different teams and initiatives, and it's not accurate to make sweeping generalizations about the company as a whole. While some decisions and products may be controversial or have unintended co…

I would love to see their "Meta alignment" dataset. They must have one, as LLaMA recognizes stuff about itself.

That's exactly what LLaMA's answer warns you not to do: a LLaMA alignment dataset does not imply a Meta alignment dataset.
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